analyzing the facebook friendship graph
TRANSCRIPT
Analyzing the Facebook friendship graph
S. Catanese1, P. De Meo1,2, E. Ferrara3, G. Fiumara1 and A.Provetti1,4
1Dept. of Physics, Informatics Section, University of Messina
2Dept. of Computer Sciences, Vrije Universiteit Amsterdam
3Dept. of Mathematics, University of Messina
3Oxford-Man Institute, University of Oxford
Int’l Conf. on Web Intelligence, Mining and SemanticsMay 26th 2011, Sogndal
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 1 / 43
Outline
1 MotivationMain objectiveThe Basic ProblemClassic Work
2 Our Results/ContributionData Extraction and CleaningData AnalysisMain Results
3 Future Issues
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 2 / 43
Outline
1 MotivationMain objectiveThe Basic ProblemClassic Work
2 Our Results/ContributionData Extraction and CleaningData AnalysisMain Results
3 Future Issues
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 3 / 43
Main objective
Extract a (partial) graph of friendship relations from FacebookI starting from the friendlist of a real userI accessing only publicly accessible data of Facebook users
using:I a wrapper (for extraction, cleaning and normalization of data)I a tool for graph visualization and analysis
developed by some of us
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 4 / 43
Main objective
Extract a (partial) graph of friendship relations from FacebookI starting from the friendlist of a real userI accessing only publicly accessible data of Facebook users
using:I a wrapper (for extraction, cleaning and normalization of data)I a tool for graph visualization and analysis
developed by some of us
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 4 / 43
Main objective
Extract a (partial) graph of friendship relations from FacebookI starting from the friendlist of a real userI accessing only publicly accessible data of Facebook users
using:I a wrapper (for extraction, cleaning and normalization of data)I a tool for graph visualization and analysis
developed by some of us
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 4 / 43
Outline
1 MotivationMain objectiveThe Basic ProblemClassic Work
2 Our Results/ContributionData Extraction and CleaningData AnalysisMain Results
3 Future Issues
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 5 / 43
Social NetworksA Taxonomy
Social Networks (SN)Described with graphs representing users and relationships amongthem
Organizational NetworksCollaboration NetworksCommunication NetworksFriendship NetworksOnline Social Networks (OSNs) [1]:
I Social Communities: Facebook, MySpace, etc.I Social Bookmarking: Digg, Delicious, etc.I Content Sharing: YouTube, Flickr, etc.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 6 / 43
Social NetworksA Taxonomy
Social Networks (SN)Described with graphs representing users and relationships amongthem
Organizational NetworksCollaboration NetworksCommunication NetworksFriendship NetworksOnline Social Networks (OSNs) [1]:
I Social Communities: Facebook, MySpace, etc.I Social Bookmarking: Digg, Delicious, etc.I Content Sharing: YouTube, Flickr, etc.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 6 / 43
Social NetworksExamples
fig1-a.png
Figure: Organizational Network
fig1-c.png
Figure: Friendship Network
fig1-b.png
Figure: Collaboration Network
fig1-d.png
Figure: Online Social Network
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 7 / 43
Mining Online Social NetworksMotivation
Is the distribution of friendship computable?
Calculating graph properties of OSNs
Exploiting new algorithms in following tasks:
I Walking through a large graph (e.g. BFS, MHRW, etc.)I Data compression (matrix decomposition, quadtrees, etc.)I Efficient visualization of large graphsI Clustering data (Fruchterman-Reingold, Harel-Koren, etc.)I Optimize efficiency in metrics evaluation (e.g. All-Pairs
Shortest-Paths related: BC, CC, diameter, etc.)
Studying the scalability of the problem
Investigating similarities between OSNs and real-life SNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 8 / 43
Mining Online Social NetworksMotivation
Is the distribution of friendship computable?
Calculating graph properties of OSNs
Exploiting new algorithms in following tasks:
I Walking through a large graph (e.g. BFS, MHRW, etc.)I Data compression (matrix decomposition, quadtrees, etc.)I Efficient visualization of large graphsI Clustering data (Fruchterman-Reingold, Harel-Koren, etc.)I Optimize efficiency in metrics evaluation (e.g. All-Pairs
Shortest-Paths related: BC, CC, diameter, etc.)
Studying the scalability of the problem
Investigating similarities between OSNs and real-life SNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 8 / 43
Mining Online Social NetworksMotivation
Is the distribution of friendship computable?
Calculating graph properties of OSNs
Exploiting new algorithms in following tasks:
I Walking through a large graph (e.g. BFS, MHRW, etc.)I Data compression (matrix decomposition, quadtrees, etc.)I Efficient visualization of large graphsI Clustering data (Fruchterman-Reingold, Harel-Koren, etc.)I Optimize efficiency in metrics evaluation (e.g. All-Pairs
Shortest-Paths related: BC, CC, diameter, etc.)
Studying the scalability of the problem
Investigating similarities between OSNs and real-life SNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 8 / 43
Mining Online Social NetworksMotivation
Is the distribution of friendship computable?
Calculating graph properties of OSNs
Exploiting new algorithms in following tasks:
I Walking through a large graph (e.g. BFS, MHRW, etc.)I Data compression (matrix decomposition, quadtrees, etc.)I Efficient visualization of large graphsI Clustering data (Fruchterman-Reingold, Harel-Koren, etc.)I Optimize efficiency in metrics evaluation (e.g. All-Pairs
Shortest-Paths related: BC, CC, diameter, etc.)
Studying the scalability of the problem
Investigating similarities between OSNs and real-life SNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 8 / 43
Mining Online Social NetworksMotivation
Is the distribution of friendship computable?
Calculating graph properties of OSNs
Exploiting new algorithms in following tasks:
I Walking through a large graph (e.g. BFS, MHRW, etc.)I Data compression (matrix decomposition, quadtrees, etc.)I Efficient visualization of large graphsI Clustering data (Fruchterman-Reingold, Harel-Koren, etc.)I Optimize efficiency in metrics evaluation (e.g. All-Pairs
Shortest-Paths related: BC, CC, diameter, etc.)
Studying the scalability of the problem
Investigating similarities between OSNs and real-life SNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 8 / 43
Mining Online Social NetworksPros and Cons
Pros:I Large-scale studies of phenomena and behaviors impossible beforeI Relations among users are clearly definedI Data can be automatically acquiredI Huge amount of information can be minedI Several levels of granularity can be established
Cons:I Large-scale mining issuesI Computational and algorithmic challengesI Online friendship 6= Real-life friendshipI Bias of data depends on visiting algorithm [2]
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 9 / 43
Mining Online Social NetworksPros and Cons
Pros:I Large-scale studies of phenomena and behaviors impossible beforeI Relations among users are clearly definedI Data can be automatically acquiredI Huge amount of information can be minedI Several levels of granularity can be established
Cons:I Large-scale mining issuesI Computational and algorithmic challengesI Online friendship 6= Real-life friendshipI Bias of data depends on visiting algorithm [2]
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 9 / 43
Outline
1 MotivationMain objectiveThe Basic ProblemClassic Work
2 Our Results/ContributionData Extraction and CleaningData AnalysisMain Results
3 Future Issues
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 10 / 43
Classic Work on (online or offline) SNs
Milgram, Travers [3]: the Small World problem (1969-70)Zachary [4]: ’mining’ and modeling real-life SNs (1980)Kleinberg [5]: the small world problem from an algorithmicperspective (2000)Golbeck et al. [6]: social networks vs OSNs (2005)Barabasi [7], Leskovec [8], Shneiderman [9], etc.: all focusing onOSNs and their analysis (nowadays)
I Online Social Network Analysis and ToolsI Large-scale data mining from OSNsI Visualization of large graphsI Bias of data acquired from OSNsI Dynamics and evolution of OSNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 11 / 43
Classic Work on (online or offline) SNs
Milgram, Travers [3]: the Small World problem (1969-70)Zachary [4]: ’mining’ and modeling real-life SNs (1980)Kleinberg [5]: the small world problem from an algorithmicperspective (2000)Golbeck et al. [6]: social networks vs OSNs (2005)Barabasi [7], Leskovec [8], Shneiderman [9], etc.: all focusing onOSNs and their analysis (nowadays)
I Online Social Network Analysis and ToolsI Large-scale data mining from OSNsI Visualization of large graphsI Bias of data acquired from OSNsI Dynamics and evolution of OSNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 11 / 43
Classic Work on (online or offline) SNs
Milgram, Travers [3]: the Small World problem (1969-70)Zachary [4]: ’mining’ and modeling real-life SNs (1980)Kleinberg [5]: the small world problem from an algorithmicperspective (2000)Golbeck et al. [6]: social networks vs OSNs (2005)Barabasi [7], Leskovec [8], Shneiderman [9], etc.: all focusing onOSNs and their analysis (nowadays)
I Online Social Network Analysis and ToolsI Large-scale data mining from OSNsI Visualization of large graphsI Bias of data acquired from OSNsI Dynamics and evolution of OSNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 11 / 43
Classic Work on (online or offline) SNs
Milgram, Travers [3]: the Small World problem (1969-70)Zachary [4]: ’mining’ and modeling real-life SNs (1980)Kleinberg [5]: the small world problem from an algorithmicperspective (2000)Golbeck et al. [6]: social networks vs OSNs (2005)Barabasi [7], Leskovec [8], Shneiderman [9], etc.: all focusing onOSNs and their analysis (nowadays)
I Online Social Network Analysis and ToolsI Large-scale data mining from OSNsI Visualization of large graphsI Bias of data acquired from OSNsI Dynamics and evolution of OSNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 11 / 43
Classic Work on (online or offline) SNs
Milgram, Travers [3]: the Small World problem (1969-70)Zachary [4]: ’mining’ and modeling real-life SNs (1980)Kleinberg [5]: the small world problem from an algorithmicperspective (2000)Golbeck et al. [6]: social networks vs OSNs (2005)Barabasi [7], Leskovec [8], Shneiderman [9], etc.: all focusing onOSNs and their analysis (nowadays)
I Online Social Network Analysis and ToolsI Large-scale data mining from OSNsI Visualization of large graphsI Bias of data acquired from OSNsI Dynamics and evolution of OSNs
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 11 / 43
Outline
1 MotivationMain objectiveThe Basic ProblemClassic Work
2 Our Results/ContributionData Extraction and CleaningData AnalysisMain Results
3 Future Issues
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 12 / 43
Mining the Facebook graphVisiting Algorithm
BFS approach: starting from a single seed (aFB profile), visiting friend-lists of nodes in orderof discovering.
Pros:
Optimal solution for unw. und. graphs
Implementation is easy and intuitive
Cons:
Introduces bias in incomplete visits
Challenges:
FB anti-data mining policies
fig2.png
Figure: Breadth-firstsearch (3rd sub-level)
1 Seed
2-4 Friends
5-8 Friends of friends
9-12 Friends of fr. of fr.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 13 / 43
Mining the Facebook graphDesign of the Mining Agent
Figure: State Diagram of the Data Mining Process
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 14 / 43
Mining the Facebook graphArchitecture
Java applicationFirefox browser embeddedXPCOM/XULRunner interface
Web pages spiderWrapper
fig10.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 15 / 43
Mining the Facebook graphHow the Agent Works
Agent Initialization:FB authentication → Seed friend-list pageSelection an example friend → XPath extractionWrapper generation and adaptationWrapper execution → Generation of the queue
Agent Execution:Load FIFO queueFor all the user profiles in the queue:
I Visit friend-list page of the current userF Extract friends (nodes) and save friendships (edges)F Insert unvisited profiles in the queue
I Visit ’next pages’ of the friend-listI Cycle the process
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 16 / 43
Mining the Facebook graphHandling DataPossible representations of vis-ited nodes and edges:
Adjacency listAdjacency matrix
fig5.png
fig11.png
Possible representation of BFSvisit for unvisited nodes:
FIFO queue
fig8.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 17 / 43
Mining the Facebook graphCleaning Data
removing duplicate nodesexploiting hash tables
relinking edges
deleting parallel edges
Data cleaning: O(n) time (optimal)
fig9.png
Structured Format: Clean datais saved under the XML structureGraphML
fig6.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 18 / 43
Mining the Facebook graphAgent Running
fig7.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 19 / 43
Outline
1 MotivationMain objectiveThe Basic ProblemClassic Work
2 Our Results/ContributionData Extraction and CleaningData AnalysisMain Results
3 Future Issues
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 20 / 43
Network Analysis MetricsTypes of Networks
Classifications of several types of networks exist. They affect metricsand maps generated in order to reflect their interpretation.
Networks member’s point of viewI EgocentricI PartialI Full
Networks entity’s point of viewI UnimodalI MultimodalI BimodalI Affiliation
Multiplex networks
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 21 / 43
Network Analysis MetricsTypes of Networks
Classifications of several types of networks exist. They affect metricsand maps generated in order to reflect their interpretation.
Networks member’s point of viewI EgocentricI PartialI Full
Networks entity’s point of viewI UnimodalI MultimodalI BimodalI Affiliation
Multiplex networks
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 21 / 43
Network Analysis MetricsTypes of Networks
Classifications of several types of networks exist. They affect metricsand maps generated in order to reflect their interpretation.
Networks member’s point of viewI EgocentricI PartialI Full
Networks entity’s point of viewI UnimodalI MultimodalI BimodalI Affiliation
Multiplex networks
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 21 / 43
Network Analysis MetricsFacebook Friendship NetworkFacebook characteristics:
Egocentric networks: the term ego denotes a person connectedto everyone (alter) in the networkUnweighted, undirected network:
I 1.0 degree
I 1.5 degree
I 2.0 degree
fig12.png
Shows a natural effect of clustering around different areas of aperson’s life: friends, classmates, workmates, family ecc.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 22 / 43
Network Analysis MetricsFacebook Friendship NetworkFacebook characteristics:
Egocentric networks: the term ego denotes a person connectedto everyone (alter) in the networkUnweighted, undirected network:
I 1.0 degree
I 1.5 degree
I 2.0 degree
fig12.png
Shows a natural effect of clustering around different areas of aperson’s life: friends, classmates, workmates, family ecc.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 22 / 43
Network Analysis MetricsFacebook Friendship NetworkFacebook characteristics:
Egocentric networks: the term ego denotes a person connectedto everyone (alter) in the networkUnweighted, undirected network:
I 1.0 degree
I 1.5 degree
I 2.0 degree
fig12.png
Shows a natural effect of clustering around different areas of aperson’s life: friends, classmates, workmates, family ecc.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 22 / 43
Network Analysis MetricsMeasures
Network metricsAllow analysts to systematically dissect the social world, creating abasis on which to compare networks, track changes in a network overtime and determine the relative position of individuals and clusterswithin the network.
Research focuses on:I Structure of the whole graph;I Large sub-graphs;I Identifying individual nodes of particular interest;I Analyze the whole graph aggregated over its entire lifetime;I To slice the network into units of time to explore the progression of
the development of the network.
A starting point: list from Perer and Shneiderman
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 23 / 43
Network Analysis MetricsMeasures
Network metricsAllow analysts to systematically dissect the social world, creating abasis on which to compare networks, track changes in a network overtime and determine the relative position of individuals and clusterswithin the network.
Research focuses on:I Structure of the whole graph;I Large sub-graphs;I Identifying individual nodes of particular interest;I Analyze the whole graph aggregated over its entire lifetime;I To slice the network into units of time to explore the progression of
the development of the network.
A starting point: list from Perer and Shneiderman
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 23 / 43
Network Analysis MetricsMeasures - Perer and Shneiderman List
Overall network metrics: number of nodes, number of edges,density, diameter ecc;Node rankings: degree, betweenness and closeness centrality;Edge rankings: weight, betweenness centrality;Node rankings in pairs: degree vs. betweenness, plotted on ascatter gram;Edge rankings in pairs;Cohesive subgroups: finding communities;Multiplexity: analyzing comparisons among different edge types.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 24 / 43
Analyzing Social NetworksVisual Analysis: Motivation
Visualizing Social NetworksConstructing visual images of social networks provides insights aboutthe structure of a network, so as representing a visual support forexplaining network phenomena [10].
Graph drawing issues:I As network complexity increases, its illegibility increases as well;I Interactive operations on nodes, such as filtering or manual
placement, are needed
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 25 / 43
Analyzing Social NetworksBetter-quality Network Visualization
Readability Metrics (RMs)RMs measure how much understandable is the graph drawing (such asthe number of edge crossings or occluded nodes in the drawing) [11].
Each algorithm attempts to find an optimal layout of the graph,often according to a set of readability metrics;A simple interim set of guidelines might aspire to the fourprinciples of NetViz Nirvana [12]:
I Every vertex is visible;I Every vertex’s degree is countable;I Every edge can be followed from source to destination;I Clusters and outliers are identifiable.
Approach: layout and filtering techniques.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 26 / 43
Analyzing Social NetworksBetter-quality Network Visualization
Readability Metrics (RMs)RMs measure how much understandable is the graph drawing (such asthe number of edge crossings or occluded nodes in the drawing) [11].
Each algorithm attempts to find an optimal layout of the graph,often according to a set of readability metrics;A simple interim set of guidelines might aspire to the fourprinciples of NetViz Nirvana [12]:
I Every vertex is visible;I Every vertex’s degree is countable;I Every edge can be followed from source to destination;I Clusters and outliers are identifiable.
Approach: layout and filtering techniques.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 26 / 43
Analyzing Social NetworksBetter-quality Network Visualization
Readability Metrics (RMs)RMs measure how much understandable is the graph drawing (such asthe number of edge crossings or occluded nodes in the drawing) [11].
Each algorithm attempts to find an optimal layout of the graph,often according to a set of readability metrics;A simple interim set of guidelines might aspire to the fourprinciples of NetViz Nirvana [12]:
I Every vertex is visible;I Every vertex’s degree is countable;I Every edge can be followed from source to destination;I Clusters and outliers are identifiable.
Approach: layout and filtering techniques.
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 26 / 43
SNA ToolsSome Powerful Tools and Libraries Adopted
GUESS focuses on improving the interactive exploration ofgraphs.NodeXL developed as an add-in to the Microsoft Excel 2007spreadsheet software, provides tools for network overview,discovery and exploration.LogAnalysis helps forensic analysts in visual statistical analysisof mobile phone traffic networks.Jung and Prefuse provide Java APIs implementing algorithmsand methods for building applications for graphical visualizationand SNA for graphs.A list of other SNA tools for extract, analyze and display socialmedia networks can be found on International Network for SocialNetwork Analysis (INSNA) site 1.
1http://www.insna.org/software/index.htmlCatanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 27 / 43
Outline
1 MotivationMain objectiveThe Basic ProblemClassic Work
2 Our Results/ContributionData Extraction and CleaningData AnalysisMain Results
3 Future Issues
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 28 / 43
Facebook Network AnalysisNodeXL - Overall Metrics
Graph Type: UndirectedVertices: 547,302Unique Edges: 836,468Edges With Duplicates: 0Total Edges: 836,468Self-Loops: 0Connected Components: 2Single-Vertex Connected Components: 0Maximum Vertices in a Connected Component: 546,733Maximum Edges in a Connected Component: 835.9Maximum Geodesic Distance (Diameter): 10Average Geodesic Distance: 5.00
Table: Overall Network Metrics
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 29 / 43
Facebook Network AnalysisNodeXL - Miscellaneous Metrics
Minimum Maximum Average MedianDegree 1 4,958 3.057 1.000PageRank 0.269 2,120.268 1.000 0.491Clustering Coefficient 0.000 1.000 0.053 0.000Eigenvector Centrality 0.000 0.003 0.000 0.000
Table: Miscellaneous Metrics
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 30 / 43
Facebook Network GraphLogAnalysis Force Directed Filtered View (25K Nodes Sub-graph)
fig7cat.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 31 / 43
Facebook Network GraphLogAnalysis Force Directed Filtered View (2.0 degree)
fig8cat.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 32 / 43
Facebook Network GraphLogAnalysis Force Directed Aggregate Filtered View (2.0 Degree)
fig9cat.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 33 / 43
Facebook Network GraphNodeXL Visualization (25K Nodes Sub-graph)
fig10cat.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 34 / 43
Facebook Network GraphNodeXL Filtered Visualization (25K Nodes Sub-graph)
fig11cat.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 35 / 43
Facebook Network GraphNodeXL Filtered Visualization (25K Nodes Sub-graph)
fig12cat.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 36 / 43
Metrics Importance: Betweenness CentralityTop 25 Nodes Ordered by BC (25K Nodes Sub-graph)
fig4.png
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 37 / 43
Future Issues
Enrich the sample (currently 5 million nodes and 15 million edges)Refine features and metrics thanks to larger sampleStudy communities emerging from the overall graphImplement parallel techniques to speed-up metrics calculationsDetermine scaling (-up and -down) coefficientsHow visiting algorithms affect extracted dataDynamic (i.e., temporal) evolution of the graph
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 38 / 43
Thank you
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 39 / 43
For Further Reading I
R. KumarOnline social networks: modeling and miningProc. of the 2nd ACM International Conference on Web Search and DataMining, 2009
M. Kurant, A. Markopoulou, P. ThiranOn the bias of BFSArxiv preprint arXiv:1004.1729, 2010
S. Milgram, J. TraversAn experimental study of the small world problemSociometry, 32(4), 1969
W. ZacharyA language for modeling and simulating social processPhD Thesis, 1980
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 40 / 43
For Further Reading II
J. KleinbergThe small-world phenomenon: an algorithm perspectiveProc. of the 32nd ACM symposium on Theory of computing, 2000
J. Golbeck et al.Social networks appliedIEEE Intelligent Systems, 20(1), 2005
A.L. Barabasi et al.Linked: the new science of networksAmerican Journal of Physics, 71(4), 2003
J. LeskovecDynamics of large networksPhD Thesis, 2008
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 41 / 43
For Further Reading IIIB. ShneidermanAnalyzing (social media) networks with NodeXLProc. of the 4th International Conference on Communities andTechnologies, 2009
L.C. FreemanVisualizing Social NetworksJournal of Social Structure, 2000
B. Shneiderman, C. DunneImproving Graph Drawing Readability by Incorporating ReadabilityMetrics: A Software Tool for Network AnalystsUniversity of Maryland, HCIL Tech Report HCIL-2009-13, May 2009
B. Shneiderman, A. ArisNetwork Visualization with Semantic SubstratesIeee Symposium on Information Visualization and Ieee Trans,Visualization and Computer Graphics 12 (5) (2006) 733-740
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 42 / 43
For Further Reading IV
Catanese, De Meo, Ferrara, Fiumara & Provetti ()Analyzing the Facebook friendship graph WIMS11 43 / 43