web intelligence and agent systems: an international journal 5...

Web Intelligence and Agent Systems: An International Journal 5 (2007) 1–5 1IOS Press

Mining the Information Architecture of theWWW using Automated Website BoundaryDetectionAyesh Alshukri and Frans CoenenDept of Computer Science, University of Liverpool, Ashton Building, Ashton Street, L69 3BX, Liverpool, UK.E-mail: [email protected], [email protected]

Abstract. The world wide web has two forms of architecture, that explicitly encoded into web pages, and that implied by webcontent and look and feel. The latter is exemplified by the concept of a website, a concept that is only loosely defined althoughusers intuitively understand it. Whatever the case the concept of a web site is used with respect to a number of applicationdomains, including website archiving and analysis. In the context of such applications it is beneficial if a website can be au-tomatically identified. This is usually done by identifying a website of interest in terms of its boundary, the so called WebsiteBoundary Detection (WBD) problem. In this paper seven WBD techniques are proposed and compared, four statical techniqueswhere the web data to be used is obtained apriori, and three dynamic techniques where the data to be used is obtained as theprocess progresses. All seven techniques are presented in detail and evaluated in this paper.

Keywords:Web structure mining, Web Graphs, Web Site Boundary Detection, Random Walk Techniques, Web page Clustering, WebArchiving, Digital Preservation

1. Introduction

A disconnect exists between the underlying struc-ture of the Web and the structure perceived by humans.Humans can intuitively identify an implied organisa-tion of the Web that is not obvious from the underlyingexplicit structure. More specifically, humans are ableto infer website boundaries in a manner that is not nec-essarily explicitly encoded in any underlying structure.Conceptually, this human perceived architecture existsin a layer above the encoded structure of the web. Theprocess whereby humans derive this architectures isfounded on relationships and/or similarities betweenfeatures encoded in the web, such as the topic or sub-ject of web pages, layout, navigation menus, imageryand so on. Although humans are able to interpret theWWW in this way, it is a difficult task for a machine toachieve. The ability for a machine to identify this un-derlying architectures remains an open problem [10].

The standard graph model of the web assumes thateach web page is a single node within a graph, withhyperlinks (edges) connecting it to other nodes. Thismodel has been used for various web analysis tasks[11,18,32]. The advantage of this standard model isthat it is explicitly encoded in the Web and it can beeasily extracted using simple web crawls, however itconveys very little information about the higher levelweb architectures that humans can perceive.

The work presented in this paper is motivated bya number of applications whereby knowledge of thehigher level web architecture, comprised of collectionsof websites each delimited by a web site boundary ofsome kind, is required. More specifically the work ismotivated for a need for automated Website Bound-ary Detection (WBD), knowledge that cannot be di-rectly obtained from simple web graph analysis. Typi-cal applications where website boundary knowledge isrequired include automated: (i) Website archiving anddigital preservation [49,1], (ii) Web directory genera-

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2 A. Alshukri and F. Coenen / Mining the Information Architecture of the WWW using Automated Website Boundary Detection

tion [30], (iii) Website map generation [18,33,34] and(iv) Web spam detection [8,9,13,31,37,51,52,53]. Fur-ther more, the study of the WWW at the website level,rather than the web-page level, provides benefits withrespect to a variety of web analysis activities [10]: (i)Content authorship analysis [17], (ii) Inter (between)and intra (within) website connectivity analysis [32],and (iii) Statistical analysis of dynamic website growthand website generation on a website level [11]. Finally,arguably the most prominent motivation for WBD isfor the purposes of digital preservation; the identifica-tion of complete web (site) content and its archivingfor future generations [1,12,16,49].

The main challenge of WBD is the resource costrequired to access, store and process web content.In terms of accessing web content there is an over-head associated with the retrieval of content; as con-tent is stored on servers around the world there isa time and bandwidth cost associated with accessingpages/resource on the web. In the context of WBD, soas to use computational resources effectively, we wishonly to consider relevant pages. The process of WBDcan be considered in either a static or dynamic con-text. The distinction is that in the static context all thewww pages to be considered are first downloaded. Inthe dynamic context only portions of the Web are con-sidered in turn, the analysis is thus conducted in a stepby step manner. The advantage of the first is that de-cisions on what groupings to classify web pages into,either contained within a website (target pages) or out-side of the website (noise pages), can be made based onall data apriori. The advantage of the second is that theamount of irrelevant (noise) pages that are requested,stored and processed can potentially be reduced, as thegathering of pages is considered at the same time asthe classifying of pages which in turn can be used toinform the decision of whether to access subsequentpages or not.

In this paper both static and dynamic WBD algo-rithms are considered. Recall from the foregoing thatthe distinction between the two is that in the first casethe search space, a collection of web pages, is obtainedin advance, while in the second case the search space isexplored dynamically. Two static WBD approaches areconsidered: (i) content based and (ii) structure based.The focus of the first, as the name suggests, is the con-tent of web pages. The idea is to identify the mostappropriate attributes for describing web page contentand to use these as features to represent web pages.Web pages are then clustered using these features inorder to identify a websites boundary. Two static con-

tent based techniques are considered: (i) the Com-posite Feature (CF) technique and (ii) the Discrimi-nant Feature (DF) technique. The focus for the staticstructure based WBD approach is the hyperlink struc-ture of web pages, as such the approach is foundedon graph partitioning methods. Two graph partitioningtechniques are considered in the context of the staticstructure based WBD approach: (i) Hierarchical GraphPartitioning (HGP) and (ii) Mincut Graph Partitioning(MGP). The proposed dynamic technique is foundedon the use of a Random Walk (RW) web graph traversalcoupled with an incremental k-means clustering mech-anism. Three variations are considered: (i) RandomWalk (RW), (ii) Self Avoiding Random Walk (SARW)and (iii) Metropolis Hastings Random Walk (MHRW).

The remainder of the paper is organised as follows.In Section 2 a literature review of some existing workon the resolution of the WBD problem is presented.Section 3 then presents a formal description of theWBD problem. Section 4 presents the first of the staticapproaches, namely the content based approach (twoalternative techniques). The second static approach,the structure based approach, is presented in section5 (two alternative techniques). Section 6 then presentsthe proposed dynamic approach to the WBD problembased on Random Walks (three alternative techniques).A complete evaluation is then presented in Section 7and some conclusions in Section 8.

2. Literature Review

The Website Boundary Detection (WBD) prob-lem is recognised as an open and difficult problemto solve [4,5,6,7,10,27,28,29]. As already noted, theWBD problem is concerned with the task of identify-ing the complete collection of web resources that arecontained within a single website. WBD broadly fallswithin the domain of “web mining” [14,21,42,50].Web mining is directed at the discovery of hidden pat-terns or knowledge in web data using techniques thatutilise web structure (hyperlinks) and web content (at-tributes of a page). This section presents some selectedrelated work which discusses a number of alternativetechniques, to that presented in this paper, that havebeen used to address the WBD problem, namely: (i)link block graphs, (ii) logical websites, (iii) compounddocuments and (iv) directory based websites.

Starting with the link block graph technique, a linkblock graph is a set of vertices and edges that repre-sent certain elements of a web page referred to as a

A. Alshukri and F. Coenen / Mining the Information Architecture of the WWW using Automated Website Boundary Detection 3

block (a coherent sets of information). Examples ofsuch blocks are a navigation menu across the top of apage, a set of related links at the side of a page or a sec-tion of text and images. Given a set of web pages a linkblock graph is typically created from these pages asfollows. First the individual pages are segmented into“blocks”. These individual blocks are created by seg-menting the HTML Document Object Model (DOM).Second a graph structure of vertices and edges is cre-ated from the blocks. Each block is a vertex. An edgebetween blocks is created based on a notion of simi-larity. The results is a link block graph. In [44], andin related work presented in [29], the link block graphconcept was used to model web data, more specificallylink blocks were used to represent structural menus (s-menus) of web pages; an s-menu is a link block graph,as described above, but describing only the internalnavigation elements of web pages. A relationship be-tween web pages exists when a link from a s-menucan be used to navigate to another page that containsthe same s-menu. Web pages that contain common s-menus can be used to define the boundaries of a web-site.

In the work by Senellart [48] the aim was to findall web pages that are determined to be contained ina “logical website”. A logical website in this contextis a collection of pages that is said to represent a logi-cal structure and content, such that they are more con-nected in terms of hyperlinks than other pages. Anidea also adopted with respect to work presented laterin this paper. The work by Senellart used the conceptof flow networks in order to discover web pages thatmade up a logical website. An assumption is made thatif a flow is pushed around the network “bottlenecks”will occur in the less connected noise pages, but flowmore freely around the highly connected target pagesof the website. To detect the boundaries of a websiteflow is pushed from a set of seed pages until a bottleneck is created around the boundaries of the website.The set of nodes that have flow pushed to them be-tween the seed and the “bottle neck” are then identifiedas part of the website. The significance of the work bySenellart is that the concept of flow networks is alsoadopted with respect to the work presented later in thispaper.

Research by Eiron [20] proposed the notion of com-pound documents; a set of web pages that can be ag-gregated in to a single coherent information entity. Asimple example of a compound document is an on-line multi-media news article. In the work by Dmitriev[17] a method to discover compound documents is

proposed. The method used supervised learning tech-niques to train a model using manually labelled com-pound documents. This method was then applied tounlabelled web pages in order to determine which ofthese pages should be aggregated to represent a com-pound document. This is a similar problem to theWBD problem, but applied in a supervised learningenvironment.

In [7] the concept of a “directory based (web) site”is described; the partition of a web server over whichsome individual (website author/owner) has full con-trol. The method proposed to address the WBD prob-lem in this case used various filters based on charac-ters and directories in the URLs of web pages so asto categorise which child pages fall under the controlof which individual and thus identifying a website’sboundaries in terms of authorship. The disadvantageof this technique is that it assumes that all pages con-tained within a website are located within the same di-rectory hierarchy as one another. In this paper it is ar-gued that this is often not the case, as individual webpages, images and other web resources can be locatedusing different urls and servers but can still make up asingle website.

3. Formal Description

The general WBD problem can be formally de-scribed as follows. A web page is defined as a WWWresource, written in HTML format, and denoted by w.A collection of n web pages is given by W such thatW = {w1, w2, · · · , wn}. A web page can be repre-sented according to both its (hyperlink) structure andits (HTML) content. In the remainder of this paper theterms page, web page, node and vertex are used inter-changeably, but in all cases we are referring to an ele-ment wi in W .

With respect to the work presented in this paperthe hyperlink structure of a set of web pages is rep-resented as a (web) graph G such that G = (V,E),where V = {v1, v2, . . . vn} is a set vertices each rep-resenting a web page (V thus corresponds to W ) andE = {e1, e2, . . . , ep} is a set of edges representingconnections between www pages (the presence of atleast one link). A specific edge is indicated using thenotation (vi, vj) where vi, vj ∈ V . A web page can ofcourse reference it self, vi, vi. Without loss of general-ity we assume that G is connected.

The HTML content of a page wi can be conceptu-alised as a set of features whereby each page has a


numerical feature vector associated with it. There ex-ists a function f : W → IRk, for some fixed in-teger t ≥ 1, such that for any wi ∈ W , f(wi) isan ordered sequence of t real numbers characterisingthe page wi. A common method to represent a setof numerical features, and the one used in this paper,is the vector-space model [45]. Using a vector spacemodel each web-page, wi, is described in terms ofa feature vector, Fi = {f1, f2, . . . , fk} of length k,which resides within some k dimensional feature (vec-tor) space. Each dimension of the feature space fi de-scribes a feature. We use the notation F to indicate thecomplete set of feature vectors associated with a setof web pages W , F = {F1, F2, . . . , Fn}. Note thatthere is a one to one correspondence between W andF (|W | = |F|).

3.1. WBD problem

Using the above formalism, the WBD problem canbe defined as follows. Given a collection of web pagesW , comprising n individual pages such that W ={w1, w2, · · · , wn}, we wish to find the sub-set of Wthat describes a “bounded” set of web pages (ω) in W ,in other words a website, centred on some given seedpage ws (typically a known home page, entry or land-ing page). The desired set ω is thus a collection of webpages that are in some sense similar to the seed pagews. The set of web pages, B, located on the “circum-ference” of ω then marks out the boundary of the website centred on ws (B ⊆ ω). The boundary set B isthus a collection of web pages where, for each wi ∈ B,wi has at least one hyperlink that connects to a webpage that is not included in ω.

A basic WBD solution used later in this paper isfounded on a clustering process. This subsection isthus concluded with a brief description of this process.Broadly, clustering is used to groups similar web pagesin W together based on their content, more specificallyon the values in an associated set of feature vectors F .A cluster configuration K = {g1, g2, . . . , gk} is es-sentially the set of pages W divided into groups (twoor more) of related or similar pages as determined bya particular clustering algorithm. One of the clustersin the set K will contain ws and is therefore identi-fied as the target cluster KT (KT ∈ K). Cluster KT

will therefore contain pages most similar to ws. The re-maining clusters, identified as KN , are the noise or ir-relevant clusters (K = KT ∪KN and {} = KT ∩KN ).The target cluster equates to ω, from which in turn wecan identify B and resolve the WBD problem.

3.2. Static vs. Dynamic

As noted in the introduction to this paper the WBDproblem is considered in both the static and dynamiccontexts. In the static context, the entire vector spaceis made available prior to the commencement of theWBD process. Thus the collection of web pages W ={w1, w2, · · · , wn} and the set of features for the webpages F = {f1, f2, . . . , fm} is known apriori. In thiscase clustering of the form described above, to pro-duce K (K = {KT ,KN}), is conducted using all theavailable data.

In the dynamic context the entire vector space is notknown in advance. The web pages in the vector spaceare populated in a dynamic manner as part of a webtraversal (crawling) process. This essentially meansthat the vector space is updated at each “step” of thetraversal process. The seed page ws is used as the start-ing point for the traversal. Prior to the start V = {} andK = {}. In the dynamic context, unlike the static con-text, during the early stages of the process the vectorspace will contain no, or only partial information, con-cerning the web pages of interest. In other words thesize of the search space, in terms of web pages, is notknown in advance. This has implications for the clus-tering process. The clustering algorithm has to makedecisions based on the web pages collected so far. Thismeans the clustering method has to operate in an in-cremental manner.

4. Content Based Static WBD

This section presents the first of the static WBD ap-proaches, namely the content based static WBD ap-proach. Recall that in the static context the searchspace is identified apriori. The content based staticWBD approach uses a range of features, extractedfrom the content and meta-data of web pages, to defineindividual web pages in the set W . A clustering algo-rithm is then applied, as described above, and the in-dividual web pages in W allocated to a set of clustersK; as noted above the cluster holding ws will then beidentified as KT . The accuracy of the WBD solutionproduced is thus subject to: (i) the features that are se-lected and (ii) the nature of the adopted clustering. Thefeatures that may considered with respect to the con-tent based static WBD approach are discussed in Sub-section 4.1 below. Two variations of the static contentbased approach are considered here, (i) the CompositeFeature (CF) technique and (ii) the Discriminant Fea-


ture (DF) technique; both are discussed in further de-tail in Sub-section 4.2.

4.1. Feature Representations

There are many features that can be extracted fromweb pages that can serve to describe content and thusbe utilised with respect to the static WBD approach.The features considered with respect to the work pre-sented in this paper are listed below. In each case weconsider our motivation for selecting the feature andhow the feature may be represented in terms of a bi-nary valued feature space representation. The featuresconsidered are categorised into two groups: (i) link(URL) based features and (ii) textual based features.

The link based features were extracted and repre-sented as follows. The HTML content for each pagewas first downloaded and validated into a well formedHyper Text Mark-up Language (HTML) DocumentObject Model (DOM) object. This was done becauseHTML documents are notoriously malformed and sub-sequently difficult to parse, for example documentsoften have missing tags. The steps required to val-idate the HTML into a well formed document aretherefore a necessary precursor to the successfully ex-traction of link based features. Each link (URL) ineach web page was then parsed and extracted fromthe HTML by splitting it into “words” using the stan-dard delimiters found in URL’s. For example the URLhttp://news.bbc.co.uk would produce the setof words {news, bbc, co, uk}. Non-textual characterswere removed (no stop word removal was undertaken).Each word then represented a binary valued feature ina feature vector representation.

The textual based features were extracted from eachweb page using a html text parser/extractor to extracttext as it would be rendered by a web browser. Thiswas deemed to be the same text that a user would useto judge a pages topic/subject. Note that for this pur-pose non-textual characters were removed, along withwords contained in a standard “stop list”. Thus theidentified textual feature values comprised only whatwere deemed to be the most significant words. Eachword represented a binary valued feature in our featurespace.

The link based features that were considered in thiswork were as follows:

Hyper links The motivation for using hyper links asone of our features is the observation that webpages that are related tend to share many of the

same hyper links. The shared links may be otherpages in the same website (e.g. the website homepage) or significant external pages (most linkspoint to a related set of pages with some commontopic).

Image links The use of image links was prompted bythe observation that web pages that link to thesame images were likely to be related, for exam-ple a common set of logos or navigation imagescould imply a relationship.

Mailto links If a set of web pages includes identicalemail links (mailto) this might also indicatethat a relationship exists.

Page Anchor links Page anchors are used to navigateto certain places on the same page, these can beuseful with respect to the resolution of the WBDproblem as they often have meaningful names. Itwas conjectured that if the same or related namesare used on a set of web pages it could imply re-lated content.

Resource links The motivation for using resourcelinks was that the styling of a page is oftencontrolled by a common Cascading Style Sheet(CSS) which could therefore imply that a collec-tion of pages that use the same style sheet arerelated.

Script links It was observed that some scripting func-tions that are used in web-pages can be writtenusing some form of common script file; if pageshave common script links then they could also berelated.

URL URLs are also likely to be an important factorin establishing whether subsets of web-pages arerelated or not. A URL is used to request the actualpage from the WWW, it contains a whole host ofinformation like server and directory information.

The textual based features considered were then:

Title text It is conjectured that the title text usedwithin a collection of web pages belonging to acommon web site is a good indicator of “related-ness”.

Body text Another key indicator of web page “relat-edness” is content; the text contained in individ-ual WWW pages.

4.2. Static Content Based WBD

This Sub-section presents the two static contentbased WBD techniques: (i) the Composite Feature


Template:1: Select feature(s)2: Apply clustering algorithm3: Extract WBD solution

Table 1Process template for the static approach to WBD.

(CF) technique and (ii) the Discriminant Feature (DF)technique. Both comprises three inter-related pro-cesses: (i) feature selection, (ii) clustering and (iii) website extraction, as illustrate by the process templategiven in Table 1. The first technique, CF, is the sim-plest of the two variations. The CF technique operatesby simply using a user prescribed individual feature, orcombination of features, to represent the web pages inW . The features (see previous section, Section 4.1) areextracted from web pages and represented as a vectorspace model. The clustering approach described aboveis then applied and the cluster containing the seed page(ws) identified as the target cluster ω (the web site ofinterest) from which the website boundary B can beextracted.

The second technique, DF, is founded on a discrim-ination method whereby individual features, and com-binations of features, are generated and evaluated us-ing a small amount of prelabelled training data (bothtarget and noise web pages). The idea is to learn a“best” set of features in terms of a given WBD prob-lem. A scoring mechanism is used for the evaluation.This is done using the score metric presented later inthis paper in Section 7.1. The discrimination processis an iterative one, commencing with single featuresand then on each iteration adding additional features.In this manner a “combination hierarchy” of featuresis produced. Given a set of features the same clusteringapproach as described above can be used to identify ωand consequently B. The process is illustrated in Table2 with an example set of features F = {a, b, c, d, e}.All pairings are considered first (line 1) and a best pair-ing identified (the paring {ac} in the example). Thisbest pairing is then used to form a set of candidatetriples (line 3), and so on.

5. Structure Based Static WBD

This section presents the second static WBD ap-proach, the structure based static WBD approach,whereby the web graph hyperlink “structure” is used toidentify the desired website boundary B. The approach

F = {a, b, c, d, e} 5-element features.1: Best {ac} : {a,b} {a,c} {a,d} {a,e} {b,c} {b,d} {b,e} {c,d}

{c,e} {d,e}2: Best {acb} : {ac,b} {ac,d} {ac,e}3: Best {acbe} : {acb,d} {acb,e}4: Best {acbed} : {acbed}

Table 2Example hierarchy generated using the feature discriminationtechnique.

makes the assumption that a website comprises a setof “strongly” connected components compared to therest of the web graph under consideration. Two varia-tions of the static structure based WBD approach areconsidered: (i) Hierarchical Graph Partitioning (HGP)and (ii) Mincut Graph Partitioning (MGP). For thefirst the Newman hierarchical partitioning algorithm[38,39] was used. For the second the Ford Fulkersonalgorithm was used which in turn is founded on themincut-max flow theorem described in [23,24] directedat the concept of flow networks. The two variationsare described respectively in Sub-sections 5.1 and 5.2below; both aim to partition the web graph such thatstrongly connected pages are segmented from the rest,thus producing a WBD solution.

5.1. Hierarchical Graph Partitioning Structure BasedStatic WBD

This sub-section describes the hierarchical graphpartitioning variation of the Structure Based StaticWBD approach founded, as noted above, on the New-man hierarchical partitioning algorithm [38,39]; anagglomerative hierarchical mechanism for identifyingclusters in graph data according to the similarity ofgraph vertices. The algorithm produces p graph clus-ters, where p is determined by the algorithm itself (noneed for user input). The hierarchical graph clusteringapproach is centred around a modularity value Q; ameasure of how “good” a given cluster configurationis. The Q value is calculated, according to the inter-connectivity of the vertices in each individual cluster,by comparing the connectivity of records within eachcluster with respect to the connectivity if the recordswere connected randomly [40].

More specifically the Q value is calculated as fol-lows. Given: (i) a directed graph G = (V,E) definedas presented above; (ii) a cluster gi comprised of a setof vertices gi = {v1, v2, . . . } (thus gi ⊂ V ); and (iii)a cluster configuration for G (K = {g1, g2, . . . , gk}).Consider Si and Sj to be two sets of vertices. A func-


tion can be devised, countEdges(Si, Sj), that countsthe number of edges from the set of vertices in Si to thevertices in Sj . Let Eij equal the set of edges from clus-ter gi to gj in the cluster configuration K representinggraph G, then:

Eij =countEdges(gi, gj)

m(1)

Thus Eij represents the set of intra-cluster edges, withrespect to gi and gj , normalised in terms of the totalnumber of edges m. Note that Eii is the set of intra-cluster edges in a single cluster gi. Let T equal the totalnumber of edges inside a given cluster configuration k,such that:

T =

gi=k∑gi=1

Eii (2)

This value is essentially the number of inter-clusterconnections for all clusters. Let ai be the number ofedges within graph G that end in the vertices containedin cluster gi:

ai =

j=k∑j=1

Eji (3)

The value a2i is the number of edges in cluster gi ifedges where connected uniformly at random in graphG. This value is simply calculated as a2i = (ai)

2.Given the above, modularity Q is then calculated us-ing:

Q =

i=k∑i=1

(Eii − a2i ) (4)

Where Eii and a2i are defined as above.The Newman clustering algorithm is an iterative hi-

erarchical clustering method applied to graph networks(such as social networks); the aim being to generate aset of clusters K, where each individual cluster con-tains one or more vertices. The input to the algorithmis a configuration K generated by placing each vertexin V in its own cluster. Thus on start up |K| = |V |.On each iteration a pair of clusters gi and gj , from allpossible combinations of clusters in K, is selected to

be joined. The selection is made by maximising the Qvalue (calculated as described above). The algorithmiteratively merges combinations of clusters so that ei-ther: (i) the modularity value (Q) is maximised for acluster configuration, or (ii) a single cluster contain-ing all input vertices V is reached. The resulting clus-ter configuration produced by the algorithm is thus thecluster configuration that has the highest value Q asso-ciated with it.

Using the Newman algorithm a set of clusters Kis produced. However, as noted above, the number ofclusters is not predetermined, it is derived by the algo-rithm. The seed page ws of interest will be located inone of the output clusters, KT . The aggregation of theremaining clusters, regardless of their number, is thenthe noise cluster KN . The aim is to produce a graphpartitioning such that KT consists of as high a numberof target vertices and as low a number of noise verticesas possible.

5.2. Mincut Graph Partitioning Structure BasedStatic WBD

This Sub-section describes the Mincut graph parti-tioning approach based on the Max-Flow Min-Cut the-orem applied to flow networks (recall that flow net-work theory was also used in [48] for WBD as disc-cused in Section 2). As noted previously, in graph the-ory a flow network is a graph of vertices and edgeswhere edges can have a flow associated with them,which is an indicator of material “flowing” betweenvertices. Flow networks can be used to model a va-riety of problems [2,25]: in the context of the workdescribed in this paper they can be used to identify“cuts” (divisions) within a network in order to producegraph partitions [46]. A “minimum cut” is a cut thatsplits a network into two partitions such that a “mini-mum” number of edges are cut, thus forming two clus-ters comprising dense (or at least denser) connections[46]. The minimum cut of a network can be found ef-fectively by applying the Max-Flow Min-Cut theorem[15,23].

The concept of a flow network model is formally de-scribed as follows (the description and examples arebased on [15]). A flow network is a directed graphG = (V,E), of the form defined previously. An edgebetween two vertices u and v (u, v ⊆ V ) is indicatedusing the notation (u, v). Each edge (u, v) has an asso-ciated non-negative capacity c defined as c(u, v) ≥ 0,and can also have an associated flow f of “material”from u to v (f(u, v) ≥ 0). The value of the flow can-


not exceed the capacity of an edge (see below). Thismodel can be conceptualised as a network of waterpipes, whereby the capacity is constrained by the di-ameters of the pipes, thus restricting the amount ofwater that can flow between any two points. One ver-tex of the flow network is considered to be the sources ∈ V and another vertex is considered to be the sinkt ∈ V ; “material” is then considered to “flow” throughthe network from the source to the sink. For a graph Gto be (typically) considered to be a flow network, someconstraints about its structure are imposed: (i) reverseedges are disallowed, if an edge (u, v) exists then theremust be no edge (v, u) in the reverse direction; and (ii)self loops are disallowed, vertex v cannot have an edgeof the form (v, v) (an edge which points back to itsself). A path p in a flow network is simply an orderedset of vertices connected by edges. A path from s to tthrough (say) two intermediate nodes u and v is rep-resented as p = {s → u → v → t}. This path thuscomprises three edges (s, u), (u, v) and (v, t). The as-sumption is made that each vertex in V is on some pathp connecting s to t. The notation ; is used to indicatea path from one vertex to another via some unspecifiedsubset of vertices in V , for example p = {s ; t}.

Using the above definition of a flow network, a flowf inside a network can be formally defined as follows.The flow f , in a flow network G, is considered to be avalue which can be measured between vertices u andv. A flow f(u, v) equals a non negative value such thatthe following constraints are met:

1. Capacity Constraint: The flow from one vertexto another must be non-negative, and must notexceed a given capacity. More formally, for alledges (u, v) ∈ E, a flow f must be ≥ 0 and can-not exceed the capacity c(u, v) of the edge alongwhich it travels.

2. Flow Conservation: The total flow into eachvertex v, must equal the total flow out of a vertexv, excluding vertex s and t. Formally:

∑v∈V

f(v, u) =∑v∈V

f(u, v) (5)

A residual network Gf consists of a network withedge capacities that show how the flow can be changedin a given flow network G. Given a flow networkG = (V,E) and a corresponding flow f , the residualnetwork of G induced by f is Gf = (V,Ef ). Ef is aset of edges representing the “residual capacity” cf of

G given the flow f . Ef may also contain edges that arenot contained in G. This occurs when back flow is ad-mitted in an opposite direction to some existing flow.An edge (u, v) has a positive value in Ef if there is acorresponding available residual capacity cf ≥ 0. Anaugmenting path is a path from vertex source s to sink tin a residual network Gf . The (augmenting) path froms to t must intuitively have a residual capacity alongits edges on which an amount of flow can be admitted,other wise the path would not exist in Gf .

A cut(S, T ) of a flow network G = (V,E) is apartition of the set of vertices V into two sets S andT , such that s ∈ S and t ∈ T . The capacity of acut(S, T ), the number of edges cut, is calculated us-ing:

c(S, T ) =∑u∈S

∑v∈T

c(u, v) (6)

Thus the sum of the capacities of edges cut that con-nect S to T . The net flow of a cut is the sum of flowsfrom S to T minus the flows from T to S. The net flowis calculated using:

f(S, T ) =∑u∈S

∑v∈T

f(u, v)−∑u∈S

∑v∈T

f(v, u) (7)

Given a flow network G = (V,E) with vertexsource s and sink t with a flow f in G, the max-flow/min-cut theorem states that the maximum flow fin a flow network G between s and t is equal to thecapacity of the minimum cut(S, T ) of G. Given a flowf in a flow network G the max-flow/min-cut is foundwhen the following (equivalent) conditions are met:

1. A flow f is a maximum flow in G2. The residual network Gf contains no augment-

ing paths3. The flow in the network f(G) = c(S, T ) for

some cut(S, T ) of G.

(1) states that a flow f needs to be a maximum flowin G. This can be proven using (2). If f is said to bea max flow, while there is an augmenting path in Gf ,then there will be a possible increase in flow such thatf can be increased and is not a max flow. (3) states thatthe total flow out of the source minus the total flow intothe source equals the capacity of some cut(S, T ).

The Ford Fulkerson Algorithm [23] is used to com-pute the max-flow in a given flow network G =


(V,E). The operation of the algorithm is straight-forward. For a flow network G, if there exists an aug-menting path in the corresponding residual networkGf from s to t, with available residual capacity cf ,flow is admitted along this path. This process is per-formed iteratively until there are no more augment-ing paths in Gf , thus f is a max-flow in G. On com-mencement of the algorithm the edges have a flow ofzero (f(u, v) = 0). The algorithm then proceeds toaugment flow along available augmenting paths, in abreadth first manner, until no more augmenting pathsexist in Gf . The resulting residual network can then beused to make a cut, such that the edges with a maxi-mum residual capacity (max-flow) are cut.

To produce a WBD solution from a graph parti-tioned into two clusters using the min cut of the grapha similar method was used to that used with respect tothe static WBD approaches described above. Namelythat the cluster containing the seed node ws is chosenas the target cluster KT , and the remaining cluster isidentified as the noise cluster KN . As noted previouslythe objective is to produce a target cluster that containsa maximum number of target pages for the website ofinterest, and a minimum number of noise pages.

The WBD approaches presented in this section fo-cused on exploiting only the hyper link structure ofW when producing WBD solutions, whilst the WBDapproach presented in the previous section (Section4) exploited only the content of the web pages in Wwith respect to the WBD problem. The approachespresented in the following section (Section 6) are di-rected at both the content and hyper link structure ofweb pages when producing WBD solutions.

6. Random Walk Dynamic WBD

This section presents the third and final WBD ap-proach considered in this paper, the dynamic approach.Recall that in the dynamic context the web data isnot available prior to the start of the analysis, the webdata is “fetched” as the approach proceeds. The threeapproaches in this section are based on the RandomWalk concept, a method of traversing a graph struc-ture in the form of a succession of random steps fromnode to node. The idea is to include an element ofserendipity (chance discovery) in the process. A ran-dom walk is used in this work as a probabilistic methodof visiting nodes (web pages) of a web graph in a non-deterministic order. It is argued in this work that such aprobabilistic method provides advantages with respect

to WBD. Thus in the context of WBD we again con-ceive of a collection of web pages, in which we wish tofind a web boundary centred on some seed page ws, interms of a graph G = (V,E) to which a random walkcan be applied. Three variations of the proposed ran-dom walk dynamic WBD approach are proposed: (i)standard Random Walk (RW), (ii) Self Avoiding Ran-dom Walk (SARW) and (iii) the Metropolis HastingsRandom Walk (MHRW).

The remainder of this section is organised as fol-lows. Sub-section 6.1 provides an overview of the dy-namic WBD approach, whilst Sub-section 6.2 providesdetails on the graph traversal methods used. The re-maining three sub-sections (Sub-sections 6.3, 6.4 and6.5) present the three variations of the dynamic ap-proach respectively.

6.1. Overview of the Dynamic WBD Approach

The generic dynamic WBD process is described bythe template given in Table 3. At the start (line 1) theset of target pages KT contains only the seed page(KT = {ws}) and the set of noise pages is empty(KN = {}). The “process internal states”, referredto in line 2, is concerned with the clustering parame-ters; thus in the case of k-means the parameters thatdictate the adjustment of the cluster centroids (proto-types). Lines 4 and 5 are concerned with the two mostimportant aspects that underpin the proposed dynamicapproaches: (i) Graph Traversal (line 4) and (ii) Incre-mental Clustering (line 5). The adopted graph traversalmethod, the random walk method, dictates the processwhereby web graph nodes (web pages) are selectedand visited, starting from the initial seed page ws. Fur-ther detail concerning the graph traversal technique ispresented in Section 6.2 below.

As the traversal progresses, for each identified nodew (web page), we determine whether the current nodebelongs to KT or KN . To do this an incremental clus-tering method was adopted. A key feature of using anincremental clustering algorithm, in the context of dy-namic WBD, is that the order in which records (nodes)are considered is subject to the nature of the traversalof G, thus the list of nodes visited will be added toover subsequent iterations and will “sooner or later”include repetitions. A new page w is added to clusterKT or KN according to its closest similarity with thecurrent cluster centroids. Note that if a previously vis-ited page is traversed, it may be reassigned to a dif-ferent cluster. The process continues (the loop fromlines 3 to 7 in the template presented in Table 3), until


the system state (cluster centroids) does not change, orsome other termination criteria is reached. Due to thedifferences in operation of the graph traversal meth-ods considered (see below) the clusters produced canvary greatly. Hence the notion of a ‘step’ is incorpo-rated into the experimental analysis of the techniques.Referring to the template presented in Table 3, a stepis considered to be a single iteration of the loop fromlines 3 to 7. The number of steps required for a dy-namic WBD algorithm to identify a WBD solution isthus a useful comparison measure.

Algorithm clustering_template (ws)1: KT = {ws}; KN = {};2: set up the process internal state;3: repeat4: select web graph node P to visit next;5: add P to KT or KN ;6: update the process state;7: until convergence;8: return KT ;

Table 3Template for the dynamic approach to WBD.

6.2. Graph Traversal

This section details the three random walk graphtraversal methods considered: (i) standard RandomWalk (RW), (ii) Self Avoiding Random Walk (SARW)and (iii) the Metropolis Hastings Random Walk (MHRW).These are all probabilistic approaches. The distinctionbetween a deterministic and a probabilistic approach isthat, given a web graph G, the first will always traversethe graph in the same manner while the second will(at least potentially) traverse the graph in a differentmanner each time the algorithm is applied to G. De-terministic approaches use a heuristic process to deter-mine which node to visit next thus the ordering of webpages accessed using a deterministic method remainsfixed for every traversal of the same graph. Thus usinga probabilistic approach there is an element of choiceand unpredictability involved (serendipity). Each ofthe proposed probabilistic graph traversal techniques isconsidered in detail in the following three sub-sectionsin the context of the template presented previously inTable 3. For the evaluation presented later in this paperthe operation of the proposed probabilistic traversaltechniques are compared with two deterministic tech-niques: (i) breadth-first Search (BFS) and (ii) depth-first Search (DFS).

6.3. Random Walk (RW)

The Random Walk (RW) method of graph traver-sal is described by the pseudo code presented in Table4. As before the input to the algorithm is a start seedpage ws. Given a current page wc (which at start upwill be the seed page) the page to be visited next isselected at random from the immediate neighbours ofwc in a conceptual web graph G. Note that the walkcan revisit nodes multiple times and consequently re-assign nodes to either KT or KN . Note also that us-ing the RW approach the process state is recomputedafter each step. Clearly any random walk on a finiteconnected graph will eventually visit all the verticesin the graph. Thus, in principle, the process could rununtil convergence is achieved (the KT and KN clus-ters become stable). However, experiments conductedby the authors (and not reported here because of spaceconsiderations) have indicated that stopping the pro-cess after a given maximum number of steps (MAXIT-ERATIONS) is more efficient and still results in a goodquality WBD solution.

Algorithm RW (ws)KT = {ws}; KN = {};set P to ws; set counter to one;set up the process internal state;repeat

redefine P to be a random neighbour of Pin G;add (or reassign) P to KT or KN ;increment counter;update the process state;

until counter > MAXITERATIONS;return KT ;

Table 4Pseudo code for Random Walk (RW)

6.4. Self Avoiding Random Walk (SARW)

The Self Avoiding Random Walk (SARW) “crawls”the graph in a random manner without returning to pre-viously visited nodes. It does this by maintaining a listof nodes R visited so far. The pseudo code is shown inTable 5. The input to the algorithm is a starting seedpage ws (as in the case of the RW technique describedabove) and a reset value r. The process commencestraversing nodes of the graph in a random fashion, withthe exclusion of the set of visited pages contained inR. The process continues until some randomly gener-


ated number (between 0 and 1) is greater than (reset)j

(were reset is a user supplied number between 0 and1, and j is a measure of the number of noise pages thathave been visited so far mitigated by the number oftarget pages visited so far) at which point the processis resumed with R = {}. Thus the more noise pagesthat are visited the more likely that the algorithm will“reset”. Note also that the reset parameter r can be ad-justed to control the sensitivity of the walk. The pro-cess continues until a predefined number of total stepsis reached.

Algorithm SARW (ws, r)KT = {ws}; KN = {};reset = r;set P to ws; set counter to one;set up the process internal state;loop

increment counter;define v0 to be an element of KT ;i = 1; j = 0; R = {v0};repeat

vi ← random neighbour of vi−1 NOTin R;P = vi;add P to R;add P to KT or KN ;update the process state;if P added to KT then

decrease j (if positive);else

increase j;end ifincrease i;

until random number > (reset)j ;end loop counter > MAXITERATIONS

return KT ;

Table 5Pseudo code for Self Avoiding Random Walk (SARW)

6.5. Metropolis Hastings Random Walk (MHRW)

The random traversal of the RW and SARW meth-ods is greatly effected by the underlying graph struc-ture. If a node has a high degree, then it is more likelyto be chosen randomly, as it has an increased numberof neighbours. The Metropolis Hastings Random Walk(MHRW) aims to reduce this behaviour. The approachtaken is to use a calculation which effectively producesan inverse probability of choosing a neighbour whichhas a high degree. The pseudo code for the MHRW isgiven in Table 6. As in the case of the previous two

random walk algorithms the input is a seed page ws.The process starts by randomly selecting neighbouringnodes to visit that have a degree below a given thresh-old value calculated using the function Deg(w) whichsimply returns the degree of the given node. Thus asthe value of Deg(wc)/Deg(wnew) increases (currentpage degree and neighbouring page degree), the like-lihood of random number > Deg(wc)/Deg(wnew)decreases, and thus there is a decreasing chance of notmoving (staying at the current node).

Algorithm MHRW (ws)KT = {ws}; KN = {};set P to ws; set counter to one;set up the process internal state;repeat

redefine Pnew to be a random neighbourof P in G;if random number >

Deg(P )/Deg(Pnew) then{dont move} P = P ;

else{move} P = Pnew;

end ifadd P to KT or KN ;incremnet counter;update the process state;

until counter > MAXITERATIONS;return KT ;

Table 6Pseudo code for Metropolis Hastings Random Walk (MHRW)

7. Evaluation

From the foregoing seven distinct technique to ad-dress the WBD problem have been presented. Fourstatic techniques, where the input data is collected inadvance, and three dynamic techniques where this isnot the case. The four static techniques were charac-terised as being either content based or structure based(two variations of each). The two variations of the con-tent based static WBD approach were: (i) Compos-ite Feature (CF) and (i) Discriminative Feature (DF).The two variations of the structure based static WBDapproach were: (i) Hierarchical Graph Partitioning(HGP) and (ii) Mincut Graph Partitioning (MGP). Thethree random walk dynamic WBD variations were:(i) standard Random Walk (RW), (ii) Self AvoidingRandom Walk (SARW) and (iii) the Metropolis Hast-ings Random Walk (MHRW). This section provides anevaluation of these seven WBD approaches.


The standard mechanism for evaluating solutions tothe WBD problem is to use hand annotated collec-tions of data extracted from the WWW [26,29,43].This method was thus also adopted with respect to thework presented in this paper. The four data sets usedfor the evaluation were departmental web pages hostedby the University of Liverpool (www.liv.ac.uk).The web pages collected were manually labelled byan assessor with respect to the WBD solution. Eachdata set contained between 450 to 500 web pages. Thefour target web sites were: (i) Chemistry (LivChem),(ii) History (LivHistory), (iii) Mathematics (LivMaths)and (iv) Archaeology, Classics and Egyptology (Li-vAce). This collection of data sets will be referred toas the Real Web Graph (RWG) collection.

The remainder of this section is organised as fol-lows. Sub-section 7.1 presents the metrics used for theevaluation. The next three sub-sections, Sub-sections7.2, 7.3 and 7.4, present the results of the evaluationof the three WBD approaches considered in isolation.Sub-section 7.5 then presents a comparative evalua-tion of the best performing WBD solutions from theforegoing evaluation. The final Sub-section presents asummary of the conducted evaluation.

7.1. Evaluation Metrics

To measure the quality of the proposed WBD ap-proaches performance score was used; the averageof the measured accuracy, precision, recall and F-measure (all standard evaluation metrics taken fromthe domain of data mining). In the case of the dy-namic approaches coverage and number of steps wasused. Coverage is a measurement of the fraction oftotal target pages CT or total noise pages CN thatare retrieved at any point in a given clustering con-figuration K. More formally, for a particular clus-tering K, the target page coverage is calculated as:coverage(K) = MTT+MNT

CT, where: (i) MTT is the

number of target pages in the target cluster and (ii)MNT is the number of target pages in the noise clus-ters. Similarly for noise pages in K is calculated as:coverage(K) = MTN+MNN

CN, where: (i) MTN is the

number of noise pages in the target cluster and (ii)MNN is the number of noise pages in the noise cluster.The number of steps is the number of steps requiredfor a dynamic WBD algorithm to arrive at an appropri-ate WBD solution as this is a good indicator of “timecomplexity”.

Fig. 1. Performance of the top nine single features, ordered from bestperforming 1, to worst performing 9.

7.2. Content Based Static WBD Evaluation

The subsection presents the evaluation of the con-tent based static approach. The evaluation was broadlydirected at evaluating the most appropriate features,and combinations of features, that could best be usedto represent web pages, with respect to WBD. Morespecifically the objectives were:

1. To analyse the performance of the CF techniquein the context of the best features to be used andthe WBD solutions generated.

2. To analyse the performance of the DF techniquein the context of the WBD solutions generated.

3. To identify the most appropriate clustering algo-rithm to be used in the context of content basedstatic WBD.

Each is discussed in further detail in the followingthree sub-sections.

7.2.1. Analysis of Composite Feature (CF) TechniqueThis subsection reports on the evaluation directed at

determining the most appropriate set of features to beused in conjunction with the CF technique. Recall, thatthis technique works by selecting a feature or combi-nation of features for WBD as presented previously inSub-section 4.2. A sequence of experiments was un-dertaken using single features, pairs of features andtriples of features. The results are presented in Figures1 and 2. Figure 1 lists the nine top performing singlefeatures ordered according to their performance score.From the Figure it can be seen that the top perform-ing features are resource, script and image links. Thesefeatures are intuitively indicative of the authors intentto express relationships in terms of a website. Resourceand script links are used to express an underlying sim-ilar “look and feel” of pages in the same collection. Itis very common for shared scripts, or blocks of codeproviding functionality, to be stored once, in a com-


Fig. 2. The performance of feature pairings, ordered from best per-forming 1, to worst performing 36.

mon location, and referred to many times by differentresources. A similar practice is used in the styling ofpages, which may link to common CSS or shared de-sign elements. From Figure 1 the features that do notseem to provide a good WBD solution are: page an-chors, text and “mailto” links.

Figure 2 shows the best results obtained when con-sidering pairs of features. In the figure the x-axis liststhe 36 possible pairings ordered according to their per-formance score. It is interesting to note that the bestperforming pairs were found to be made up of com-binations of a strong and weaker single features. Theresults demonstrate that when using pairings better re-sults are obtained than when using single features.With respect to the double combinations, script, im-age and resource links appear in the top performingcombinations. In contrast to what might have been pre-dicted, combining the top performing single featuresdoes not necessarily yield top performing combina-tions. The features that performed the best with respectto the single feature experiments, do however, seem toperform much better when combined with features thatappear lower in the performance list generated with re-spect to the single feature analysis. The most effectivelower performing single features, when used in combi-nation with higher performing single features, appearto be: url, mailtolinks and title features.

The results from the triple feature combinations, notshown here, were also evaluated. It was observed thatthe results obtained were consistent with the results ob-tained using pairs of features; under performing fea-tures appear to increase the performance of the highperforming features when used in combination. In con-clusion the top performing features are resource, scriptand image links.

Fig. 3. The best WBD performance score for each feature combina-tion size, the highest scoring combination for each data set is high-lighted by a dot.

7.2.2. Analysis of Discriminant Feature (DF)Technique

This sub-section presents the results obtained fromthe evaluation conducted to determine WBD solutionsusing the DF technique. Recall that the CF techniqueuses a predefined set of features and, as demonstratedin the foregoing sub-section, there is no best set offeatures guaranteed to produce a best WBD solution.The DF technique addresses this issue by using a smallamount of training data whereby a best set of featurescan be learnt. The DF technique iteratively combinesfeatures, starting with a single feature, until a best setof features is arrived at which can then be used to de-rive a WBD solution in the same manner as in the caseof the CF technique. On each iteration, the combina-tion that provides the biggest increase in performancescore with respect to the WBD solution is chosen to bethe “feature set so far”.

The training data that is used for the DF techniquewas a labelling of pages within the website bound-ary solution for each of the input data sets. Recall thatas the DF technique progresses this labelled data wasused to evaluate the best combination of features, suchthat a best final feature combination was derived whichcould then be used to generate a WBD solution. Oneach iteration the resulting WBD solutions are com-pared to the known training data solution and the bestperforming feature combination selected for use in thenext iteration. This iterative process builds combina-tions of features. The results obtained are given in Ta-ble 4 which shows the best feature combination, andcorresponding performance score, from each iterationas the DF algorithm progresses (with respect to theRWG evaluation data sets). From the table it can be


Fig. 4. The best performing feature combinations and performancescores for each RWG data set. The highest scoring feature combina-tion in each case has been highlighted.

seen that, as expected, the highest performing featurecombinations contain features that were identified ashigh performing using the CF approach. Namely re-source, script and image links.

Figure 3 shows the same results as presented in Fig-ure 4 but in the form of line graphs. In the figure 4performance score is listed on the y-axis and size offeature combination, from 2 to all 9, along the x-axis.From the figure it can be seen that by using all featuresin combination produced the worst performing WBDsolution with respect to all four test data sets (probablybecause the representation got to convoluted).

The results presented in Table 4 and Figure 3 corrob-orate the results presented in Sub-section 7.2, namelythat the best feature combinations are those that com-prise both at least one high and at least one mid per-forming single feature. It can also be concluded thatthere always exists a combination of features that givesa highest WBD performance score, and that the size ofthis best combination is somewhere between three andseven.

Fig. 5. Clustering algorithms ranked according to average WBD per-formance score across the RWG data set using single, double andtriple feature combinations.

7.2.3. Clustering AlgorithmsIn this sub-section the results from experiments con-

ducted to identify the most appropriate clustering al-gorithm to be used are presented. Four clustering algo-rithms were considered as follows:

1. Kmeans (Centroid based) [36]2. Bisecting Kmeans (Hierarchical based) [54]3. DBScan (Density based) [22]4. KNN (Centroid based) [19]

Figure 5 shows the ranked average WBD perfor-mance score, with respect to each clustering algo-rithm, with respect to the four datasets considered andusing single, double and triple feature combinations.Both the Kmeans clustering algorithm and the bisect-ing Kmeans algorithm require the number of clustersto be pre-specified. In contrast, KNN and DBScan donot use such a value, instead they rely on a user pro-vided similarity threshold value to determine a clusterconfiguration. In the evaluation presented in this sec-tion the parameters for each algorithm were systemat-ically varied and the best performing output reported.The best performing parameter in each case was as fol-lows: (i) for Kmeans the best number of clusters wask = 3, (ii) for bisecting Kmeans k = 4 produced thebest results, (iii) for KNN the most appropriate userdefined threshold was found to be c = 5, and (iv) forDBScan the most appropriate distance value was foundto be d = 0.3 and the minimum number of pointsm = 5. Inspection of Figure 5 shows that the Kmeansalgorithm produced the best overall performance, ar-guably because of its robust operation, allowing it tobest adapt to the various feature spaces.

The main problems with applying clustering algo-rithms to the different kinds of feature space used inthis work is the so called “curse of dimensionality”;whereby, as the number of dimensions increases it be-


comes increasingly computational intensive to evalu-ate distance functions with respect to the feature space.The fact that the feature space becomes very sparseas the number of dimensions increases can be consid-ered to be a contributing factor to the poor performancewhen all features are used.

7.3. Structure Based Static WBD Evaluation

The evaluation of the two variations of the structurebased static WBD approach is presented in this sub-section: (i) Hierarchical Graph Partitioning (HGP) and(ii) Mincut Graph Partitioning (MGP). Recall that thefirst was based on Newman hierarchical graph parti-tioning (see Sub-section 7.3.1), while the second wasbased on the minimum cut algorithm which in turn wasbased on min-cut max flow theorem (see Sub-section7.3.2). The objective of the evaluation presented in thissection is to measure the performance of the two struc-ture based approaches (HGP and MGP) with respectto the WBD solutions produced. The evaluation resultsfor each technique are presented and discussed in fur-ther detail in the following two sub-sections. The re-sults, in both cases, indicated that the structure basedstatic WBD approach has potential in terms of produc-ing high quality WBD solutions. The results obtainedalso demonstrated that the connectivity between ver-tices of a single website boundary are in fact encodedin the underlying web graph structure.

7.3.1. HGP evaluationThe evaluation of the HGP technique was conducted

by measuring the performance of the Newman HGPalgorithm using a sequence of web graph snapshotsvarying in size from 50 to 450 vertices increasingin steps of 50.The pages contained in each snapshotwere collected using a breadth first crawl from a seed(home) page ws, thus the ratio of noise to target pagesincreased as the number of vertices considered in-creased. The aim was to mimic the way that a webcrawler might collect data.

The results are presented, in terms of performancescore as before but also precision and recall, in Fig-ure 6. From the figure it can be seen that as the snap-shot size increased recall also increases while preci-sion decreased. The increase in recall indicated a cor-responding increase in the number of target web pagesgrouped with the seed page. This in turn demonstratedthat, as the snap shot size increased, the Newman algo-rithm was increasingly partitioning the graph in such away that target pages were not being allocated to too

Fig. 6. The results obtained using the HGP technique on the RWGdatasets (LivChem, LivHistory, LivMath and LivSace) with varyingsnapshot sizes.

many differing clusters, but were in fact being mostlygrouped together (the desired result). The decrease inprecision indicated that the number of noise pagesgrouped with the seed page increased as the snapshotsize increased. In other words as the snapshot size in-creased the HGP technique was increasingly partition-ing the graph in such a way that pages belonging tothe website of interest (defined by ws) were groupedtogether but with increasing amounts of noise pages.

The results presented in Figure 6 also indicate thatthe most appropriate WBD solution associated witheach data set is not produced using a single particularsnapshot size (number of vertices/pages). What is con-sistent is that the best WBD solution is arrived at us-ing smaller sized snapshot than larger sized snapshots.


This intuitively indicates that reducing the amount ofnoise improves the WBD solution using the proposedHGP technique. What this suggests in terms of the de-ployment of the technique is that an improved crawl-ing strategy, that serves to reduce the number of noisepages included in the snapshot, such as that employedby the random walk techniques also considered in thispaper, will be beneficial in the context of derivingWBD solutions. In the figure it can also be observedthat there is some volatility in the results. The preci-sion values of LivChem and LivMaths drops for cer-tain data set sizes, as does the score in some cases.This is due to the nature of the graph partitioning al-gorithm working on the hyperlink structure of vary-ing sized snapshots. These snapshot sizes could con-tain a vastly different hyperlink structure if they con-tain or omit a high degree page (has many links to otherpages). This then then cause the HGP technique to seg-ment the graph in a very different set of partitions. Dueto this there is volatility in the results, as shown in fig-ure 6.

7.3.2. MGP EvaluationThe evaluation of the proposed MGP technique was

directed at analysing the Mincut algorithm’s ability tosegment a graph using the concept of flow networks soas to generate a WBD solution. Recall that the Mincutalgorithm uses a source s and a sink t vertex within thegraph to conceptualise flow, and then makes a cut ofthe network at the “bottle necks”. For the evaluation ofthe MGP technique the source s and sink t were iter-ated over all the possible vertices in the RWG data sets.Thus for a graph containing n vertices this involvedn×n combinations (s and t can be at the same vertex).The evaluation was therefore exhaustive.

The results are presented in Figure 7. The figureshows the performance scores for each of the WBDsolutions along the y-axis and the identifier for eachpossible vertex combinations of s and t along the x-axis. The x-axis is ordered according to the source/sinkcombination, in BFS order from the seed node, whichis displayed at point 0 on the x-axis. From the figure itcan be observed that for each of the datasets there ex-ists at least one WBD solution that has a performancescore of > 0.9. Analysis of the top performing re-sults (after WBD solutions have been generated) revealthat best results were obtained when the sink vertex twas selected from amongst the target website vertices(pages within the website boundary). There was no sig-nificant difference in performance regarding whetherthe source vertex s was selected from amongst the tar-

Fig. 7. WBD performance scores using the MGP technique with it-erating source s and sink t vertices. (performance score on y-axiscombination identifier on x- axis).

get or noise vertices. Thus, in practice, when deployingthe MGP technique ws should be selected as the sinkvertex t; this is contrary to intuitive thinking where thesource would be identified as the seed page.

7.4. Random Walk Evaluation

This section presents a comparative evaluation ofthe three proposed dynamic probabilistic random walk


techniques for WBD: (i) RW, (ii) SARW and (iii)MHRW. The reported evaluation was conducted bycomparing the proposed techniques with two de-terministic techniques, namely Breadth First Search(BFS) and Depth First Search (DFS). Recall that theproposed dynamic approaches are based on randomwalk traversals of the web graph, combined with in-cremental clustering of web pages to produce a WBDsolution. The objectives of the evaluation were:

1. To compare the operation of the considered tech-niques in the context of the most appropriate fea-tures with which to represent web pages. Forthis purpose five single features were considered:body text, title text, script links, resource linksand image links. These features were chosen be-cause they were the top performing features iden-tified with respect to the analysis presented inSection 7.2 above. Note that, for simplicity, onlysingle features (in contrast to combinations ofdoubles or triples) were used with respect to theevaluation results presented here.

2. To compare the performance of the five tech-niques in terms of runtime and coverage.

3. To compare the operation of two different clus-tering algorithms, the Incremental Kmeans (IKM)algorithm[41,47] and the Incremental ClusteringAlgorithm (ICA) [35], in the context of the tech-niques under consideration.

With respect to the first objective the results are pre-sented in Figure 8. The results demonstrate that the ti-tle feature exhibits the best performance in terms of theproposed dynamic WBD solutions. The performanceof the title feature in the dynamic context contrasts tothat of the static context (see Sub-section 7.2). The rea-son why the title feature performed well in the con-text of the dynamic WBD approaches, whereas it didnot perform so well in the context of the static CF ap-proaches, was found to be due to the nature of the in-cremental clustering and web crawling used. The dy-namic approaches were found to be significantly influ-enced by the order in which web pages were receivedwhich in turn was a consequence of the adopted webcrawl strategy used.

The advantages offered by usage of the title featurewere: (i) it is written by the author/publisher to conveysome summary of the page that is quickly digestibleby users, (ii) it is focussed on a particular subject, (iii)it is written in a concise manner designed to “round-up” the subject of the www page, (iv) it contains muchless noise than (say) the body text of a www page, (v)

Fig. 8. Average WBD Performance score for dynamic techniquesusing Body text, Title text, Script links, Resource links and Imagelinks as the feature feature representation.

it is short (few words are typically used) and (vi) itis focussed on a particular subject. As a result usageof www page titles as a feature with which to repre-sent www pages offers good similarity measurementbetween related pages. The image, script and resourcelinks performed the worst in the dynamic context be-cause they did not provide for effective comparison toallow target and noise pages to be distinguish (at leastin the dynamic context). It is suggested that the na-ture of the dynamically changing feature space is thereason for the difference in performance between thestatic and dynamic approaches founded on the samefeatures.

In the context of the evaluation with respect to thesecond objective for the dynamic approach to WBDonly the title feature was considered (because the pre-vious experiments had indicated this provided the bestperformance). The runtime results are presented in Ta-ble 7 where the rows represent the five dynamic tech-niques considered. The first three columns give theKT , KN and total coverage results obtained on termi-nation. The fourth column lists the best performancescore (average are given in Figure 8), whilst the fifthand six columns give the average runtime per step inseconds and the overall runtime in terms of the numberof steps required to obtain a best performance. Fromthe table it can be seen that the best performing tech-nique in terms of performance score was the proposedMHRW technique (performance score of 0.791). TheBFS and DFS techniques produced the best run timesthey traverse the graph structure in linear time.

Figure 9 gives plots of the average coverage, tar-get coverage and noise coverage as the dynamic tech-niques proceed (number of steps along x-axis). Thefigure shows how the coverage values change overtime. From the figure it can be seen that the graph cov-erage using the RW reaches a high level earlier on inthe process in comparison with SARW and MHRW.


MHRW is the slowest at covering the total graph al-though the MHRW technique covers noise pages at alower rate whilst maintained a high coverage of tar-get pages. Note that the randomised traversal usingRW, SARW and MHRW are all subject to the underly-ing structure of the graph, the hyperlink structure canbe complex, and unpredictable. The MHRW approachdeals well with the complex structure and producesthe best WBD score *** NOT ACCORDING TO FIG-URE 8 ***.

Table 7WBD performance for the dynamic techniques considered, using theRWG data sets, ordered according to performance score.

Coverage Time(ms) TotalTarget Noise Total Score /Steps Steps

MHRW 0.941 0.768 0.788 0.791 4.740 25000

BFS 1.000 1.000 1.000 0.717 314.801 842

DFS 1.000 1.000 1.000 0.714 320.561 842

RW 0.960 0.992 0.988 0.632 5.957 25000

SARW 0.890 0.869 0.871 0.506 5.611 25000

From the foregoing reported evaluation It was con-cluded that the MHRW approach was the best perform-ing in terms of performance score. The approach pro-duced an acceptable WBD solution, while at the sametime reducing the amount of noise pages covered. Thiseffectively reduces the resources required to produce aWBD solution.

The third and final objective of the evaluation di-rected at the dynamic approaches was to compare theusage of IKM and ICA clustering algorithms in thecontext of dynamic WBD. The average WBD perfor-mance score of the BFS and Random Walk (RW) ap-proaches, using ICA and IKM, are shown in Figure 10.The BFS and RW techniques was chosen as a compar-ison of a probabilistic (RW) and deterministic (BFS)technique. The Figure 10 show the “history” of thegraph traversal using both clustering algorithms as thepages of the graph were visited step by step. The his-tory of the walks show how the performance of theWBD solutions changes as the walk proceeds. TheBFS technique of traversing the graph produced an or-dering of pages such that both the ICA and IKM al-gorithms effectively converge in under 2k steps. TheRW method of traversal, however, produces an order-ing of pages such that the pages were constantly beingrandomised therefore the algorithms do not convergewithin 10k steps. From the figure it can be seen that

Fig. 9. The average total coverage (top), target coverage (middle)and noise coverage (bottom) for the five dynamic WBD techniquesusing the RWG data sets and the title feature.

the score of the WBD solution produced by the BFStraversal using IKM algorithm outperform that of theICA (Figure 10), this indicated that the IKM algorithmgroups pages in a more beneficial way compared toICA in terms of WBD.

7.5. Comparison of proposed approaches

This section presents an overall comparison of thethree WBD approaches presented in this paper. Recallthat the above reported evaluations were undertaken interms of WBD performance score and coverage andrun time. The WBD performance score was used tomeasure how representative the website boundary so-lution was with respect to each approach. Graph cov-erage was used with respect to the dynamic approachand was used to measured the amount of data gathered(noise and target pages) to produce a WBD solution,


Fig. 10. The average WBD performance score of BFS and RW tech-niques using either IKM or ICA clustering.

which in turn provided an indicator of the potential re-source cost associated with requesting, downloadingand pre-processing data from the web. Run time wasused to measure how long each technique took to pro-duce a WBD solution. The amount of time taken whenanalysed using the number of steps was used to furthercomparatively evaluate the techniques presented in thiswork. The foregoing evaluation also considered the ef-fect of using various parameters and variations. For theoverall comparison reported on in this Sub-section thebest performing variations of each of the proposed ap-proaches was used as follows:

– Static Technique: DF– Static Technique: MGP– Dynamic Technique: MHRW using IKM

As before the evaluation was again conducted usingthe RWG data collection.

The results are presented in Figure 11. In the fig-ure three bar graphs are presented, performance score,target coverage and noise coverage with respect to theRWG data sets. From the figure it can be seen that thestatic approaches performed much better than the dy-namic approaches in the context of both performancescore and coverage. The highest performing WBD so-lutions were produced using the DF and MGP tech-niques. This outcome would be expected as the staticapproaches have access to all the data apriori (assum-ing a large enough sample); the static approaches canthus use all the data to make relative decisions on whatpages are included with respect to the WBD problem.In the dynamic context it is not the case that all datais known in advance, only partial (step-by-step) datais used to produce a WBD solution, but as the pro-cess proceeds we can expect the approach to get bet-ter. Therefore the decisions made using the dynamicapproach are made relative to what is known aboutthe partial data. It can be argued that the dynamic ap-

Fig. 11. Comparison of the highest performing approaches for theproduction of WBD solutions. Sub-Figure (Top) shows WBD per-formance score, (Middle) Target and (Bottom) Noise coverage.

proach produces an adequate WBD solution while atthe same time maximising the number of target pagesgathered (Figure 11 Middle) and reducing the num-ber of unwanted noise pages (Figure 11 Bottom). If astandard cost is to be associated with each web page,which is consistent with the requesting, downloadingand pre-processing, then it can be seen from the com-parative evaluation that the dynamic approach providesa much lower cost WBD solution, while still main-taining an adequate WBD performance. This is shownby the higher WBD score, and lower noise coverage(which is associated with a higher cost).

8. Conclusion

In this paper a number of approaches/techniques foraddressing the WBD problem have been proposed andevaluated. The significance of WBD is that it has ap-


plication with respect to automated website archivingand digital preservation to give two examples (see Sec-tion 1). The work presented in this paper consideredboth static and dynamic approaches for resolving theWBD problem. The distinction is that in the static con-text all the www pages to be considered are first down-loaded. This has the consequent advantage that deci-sions on what groupings to classify web pages into, ei-ther contained within a website (target pages) or out-side of the website (noise pages), can be made basedon all data apriori. In the dynamic context only por-tions of the Web are considered in turn, the analysisis thus conducted in a step by step manner. This hasthe consequent advantage that the number of irrelevant(noise) pages that are requested, stored and processedcan potentially be reduced. The evaluation of the pro-posed approaches presented in this work concentratedon an important challenge with respect to WBD whichis the resource cost required to request, store and pro-cess web content (see Section 1). This was conductedby evaluating the noise and target page coverage. Theexperimental analysis and evaluation of the static ap-proaches was used to inform the dynamic approach toWBD. The static feature combination and graph par-titioning analysis informed the dynamic approaches.The dynamic approach exploited the structural prop-erties of the web graph while also using the contentbased attributes of web pages with respect to WBD.

The four static approaches, CF, DF, HGP and MGPwere considered (and evaluated): content based (CFand DF) and structure based (HGP and MGP). Theevaluation of the content based approach concentratedon the feature representation of web pages. The eval-uation concluded that the most appropriate features tobe used to represent a web page in the context of pro-viding a good WBD solution were: image, script andresource links. The most appropriate clustering algo-rithm was shown to be Kmeans. The structure basedapproach concentrated on the structural properties ofthe hyperlinks in the web graph for the purpose ofWBD. The evaluation concluded that hyperlinks couldbe successfully exploited with respect to WBD. Theevaluation of the static approaches indicated that theMGP technique, which used the max-flow min-cut the-orem, was the most effective structure based WBD ap-proach.

The three dynamic probabilistic random walk tech-niques were considered: (i) RW, (ii) SARW and (iii)MHRW. In the evaluation the operation of these tech-niques was compared to two deterministic techniques,namely Breadth First Search (BFS) and Depth First

Search (DFS). From the evaluation it was concludedthat the MHRW approach was the best performing interms of performance score. The approach producedan acceptable WBD solution, while at the same timereducing the amount of noise pages covered. This ef-fectively reduces the resources required to produce aWBD solution.

Overall this work argues that the dynamic approachusing Random Walk graph traversal and incremen-tal Kmeans clustering provides for both an effectiveWBD performance and a minimisation of the numberof irrelevant noise pages considered, which is increas-ingly important if we wish to scale the proposed dy-namic approach to much larger websites. In particu-lar the Metropolis Hastings Random Walk (MHRW)graph traversal method, coupled with the title featurerepresentation and the incremental Kmeans algorithm(IKM), was the best performing WBD method.,

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