social learning and knowledge sharing technologies lecture slides about social patterns & graph...
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Lecture Slides from the 2nd Lecture in "Social Learning and Knowledge Sharing Technologies" about Social Patterns & Graph Theories Lecture at TU Darmstadt - Multimedia Communications Lab Lecturers: Johannes Konert & Christoph RensingTRANSCRIPT
© author(s) of these slides including research results from the KOM research network and TU Darmstadt; otherwise it is specified at the respective slide
31-Oct-14
Dr.-Ing. Johannes Konert
Dr.-Ing. Christoph Rensing
KOM - Multimedia Communications Lab
Template Teaching v.3.4
KnowShare__3_SocialDesignPatterns_GraphTheory__2014.10.31__v1.1.pptx
Social Patterns &
Graph Theories Basics
Social Learning and Knowledge Sharing Technologies
31.10.2014
1. Theories and Challenges
2. Structures and Pattern
Modeling Context
4. Context-Awareness
Search Context Detection
3. Services and Mechanisms
Peer Tutoring Collabora. Tasks
Contextual Services
5. Evaluation
Foundations and Learning Theories
Challenge: Resource Selection & Navigation
Challenge: Coopera-tion & Collaboration
Challenge: Feedback & Targeting
Peer Assessment & Feedback Learning
Analytics
Learning Path Transparency
Offline Evaluation
Hypothesis validation
Formative and summative
Resources
Social Patterns
Graph Theory Basics
Scripted Collaboration
Re- com- men- der
Human
Resource User / Learner
KOM – Multimedia Communications Lab 2
Approaches to Modern
Web Application Development
MVC, ACID, CRUD REST,
LAMP <-> MEAN, PaaS
Social Media Systems Design Aspects
Content vs. User
Relationship Types
Roles, Levels, Badgets, Achievements
as an instrument for Guidance
Responsibility and Democracy
Ambient Intimacy
Graph Theory Basics
What have
subways, emails and rivers in common?
(or users, tags, resources)
Image sources: http://www.seawaterfoundation.org/siteImages/rivers_art.jpg,, http://vnfa8y5n3zndutm1.zippykid.netdna-cdn.com/wp-content/uploads/2011/12/url7.jpg, http://images.all-free-
download.com/images/graphiclarge/s_bahn_71263.jpg, http://de.roblox.com/item.aspx?seoname=U-Bahn&id=28172595, http://faculty.kutztown.edu/rieksts/225/graphs/tripartite_files/image002.jpg,
Lecture 3
Social Patterns & Graph Theories Basics
KOM – Multimedia Communications Lab 3
Motivation
1. Challenge: Resource Selection
& Navigation
4. Challenge: Cooperation & Collaboration
2. Challenge: Targeting
(How to find resources? How to navigate?)
How to motivate to reach learning goals?
Modern Web App Dev (Basics)
Social Systems Design Patterns
Graph Theory (Basics)
3. Challenge: Feedback
(How to design Peer Feedback/Assessment?)
(What is the path to the goal?)
(Who is the best candidate?)
How to establish a “community” sense?
Challenges
How to tell “what’s next”?
KOM – Multimedia Communications Lab 4
At the end of the lecture / exercise you will be able…
Learning objectives of lecture 3
..to repeat aspects to keep in mind when designing a new Social Learning and Knowledge Sharing System.
..to decide based on the aspects which components you want to use.
..to select and focus on specific Social System Design Patterns to support your system characteristics.
..to differentiate (basic) types of graph representations and you can decide and explain to which type example graphs belong to
KOM – Multimedia Communications Lab 5
Approaches to Modern
Web Application Development
Image source: ok/FreeDigitalPhotos.net
Placement in the context of the lecture
1. Theories and Challenges
2. Structures and Pattern
Modeling Context
4. Context-Awareness
Search Context Detection
3. Services and Mechanisms
Peer Tutoring Collabora. Tasks
Contextual Services
5. Evaluation
Foundations and Learning Theories
Challenge: Resource Selection & Navigation
Challenge: Coopera-tion & Collaboration
Challenge: Feedback & Targeting
Peer Assessment & Feedback Learning
Analytics
Learning Path Transparency
Offline Evaluation
Hypothesis validation
Formative and summative
Resources
Social Patterns
Graph Theory Basics
Scripted Collaboration
Re- com- men- der
Human
Resource User / Learner
KOM – Multimedia Communications Lab 6
Codecademy Airbnb
Examples of Modern Web Applications
Characteristics (some..)
Changes in one GUI widget cause reload/filtering of data in other app parts
Far beyond text-based websites
Responsive Layout
KOM – Multimedia Communications Lab 7
.. with Web Application we mean: an application running (and displayed) in the browser
.. with Modern we mean: system design solutions supporting development, maintenance,
performance and responsiveness of web applications beyond and in contrast to websites.
..with Approaches we mean: Technologies and Paradigms used to develop Modern Web
Applications (this excludes hardware and runtime maintenance aspects)
Components of
a Modern Web Application
Image source: http://www.codeproject.com/Articles/645753/Challenges-and-solutions-Architecture-of-a-Modern
Approaches to Modern Web Application Development
Distributed System(s)
..so forget this illustration
KOM – Multimedia Communications Lab 8
Web server
Components of a (Modern) Web Application
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Model Controller
<!DOCTYPE html>
<html>..</html> View
Web browser
Script language Local state
KOM – Multimedia Communications Lab 10
MVC (Pattern)
Model encapsulates the data (objects) state
View displays the data, is user interface and allows user actions
Controller reacts on user actions, coordinates model(s) and system
communication
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Web browser
Script language Local state
Model Controller View Model Controller View
KOM – Multimedia Communications Lab 11
MVC (Pattern)
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Web browser
Script language Local state
Model Controller
View Model Controller
View
KOM – Multimedia Communications Lab 12
ACID (Database Design Properties)
Atomicity (all or nothing)
Consistency (constraint-based valid states)
Isolation (concurrency control)
Durability (no loss after commit)
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Web browser
Script language Local state
Model Controller
View Model Controller
View
KOM – Multimedia Communications Lab 13
CRUD (Persistent Storage [Interface] Properties)
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Web browser
Script language Local state
Model Controller
View Model Controller
View
Operation SQL HTTP
Create INSERT PUT / POST
Read (Retrieve) SELECT GET
Update (Modify) UPDATE PUT / PATCH
Delete (Destroy) DELETE DELETE
We
b S
erv
ice
/ A
PI
KOM – Multimedia Communications Lab 14
REST (Property)
Representational state transfer
Stateless server & cachable responses
uniform ressource and service addresses
Alternative representations (?)
Interface-based operations
(identify, create, modify, delete)
New aspects:
Hypermedia as state transition machine
Using many HTTP methods
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Web browser
Script language Local state
Model Controller
View Model Controller
View
We
b S
erv
ice
/ A
PI
HTTP-based example: http://www.airbnb.com/places public class MyPlaces {
@GET
@Produces(MediaType.TEXT_PLAIN)
public String getIt() {
return "Darmstadt, Frankfurt, München";
}
@GET
@Produces(MediaType.APPLICATION_JSON)
public String getIt() {
return "{ ‘places‘:
[‘Darmstadt‘,‘Frankfurt‘,‘München‘]}";
}
}
KOM – Multimedia Communications Lab 15
LAMP (Paradigm)
Linux OS
Apache Webserver
MySQL DB
PHP Server-side Language
(Also popular as XAMP or XAPP)
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Web browser
Script language Local state
Model Controller
View Model Controller
View
We
b S
erv
ice
/ A
PI
KOM – Multimedia Communications Lab 16
MEAN (Paradigm)
Node.js operating language
Express Webserver framework
MongoDB NoSQL storage
AngularJS client-binding
(Also known as AMEN)
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
Script language DB
Operating System (OS)
Web browser
Script language Local state
Model Controller
View Model Controller
View
We
b S
erv
ice
/ A
PI
New aspects:
MEAN adds a client-layer component to
the stack
All JavaScript
KOM – Multimedia Communications Lab 17
Total number based on Wikipedia entries on ‚database‘, ‚webserver‘, ‚web application framework‘,…
from 2014-10-29 (only to get a idea of dimensions)
Modern Web Application Development
Database
(~50)
Webserver
(~30)
Server: Web App
Framework (~130)
Template
Engine (~90)
Client: Web
App Framework
(JS: ~40)
SQLite
HyperSQL
MySQL
PostgreSQL
Cassandra
MongoDB
(Microsoft IIS)
Apache HTTP
Apache Tomcat
Jetty
Boa
NginX
Mongoose WS
lighttpd
(Node.js)
Full solution frameworks:
ASP.NET MVC, GWT
PHP (CakePHP, Zend)
Ruby (on Rails)
Python (Django, Pyramid)
Java Servlets (Spring, JSF, Struts)
ExpressJS
PHP / Smarty
Genshi
Cheetah
Mustache
JSP
Jade
Dojo
MochiKit
script. aculo.us
ExtJS
YUI
Qooxdoo
jQuery
Ember.js
AngularJS
KOM – Multimedia Communications Lab 18
PaaS (Pattern)
Platform as a Service
Cloud-Service model for delivery
of a (scalable, reliable) operating
platform for applications
Client creates and maintains
application
Modern Web Applications
Image sources: http://www.computero.com/media/HP-server.jpg, DryIcons/Shine, Tango IconSet
Web server
Server Client
Operating System (OS)
DB
Operating System (OS)
Web browser
Script language Local state
Model Controller
View Model Controller
View
We
b S
erv
ice
/ A
PI
Script language
https://www.heroku.com/ https://cloud.google.com/appengine/ https://www.openshift.com/
KOM – Multimedia Communications Lab 19
Social System
Design Patterns
Image and book reference: http://www.amazon.com/Designing-Social-Interfaces-Principles-Experience/dp/0596154925, http://www.amazon.com/Building-Social-
Applications-Gavin-Bell/dp/0596518757/
Placement in the context of the lecture
1. Theories and Challenges
2. Structures and Pattern
Modeling Context
4. Context-Awareness
Search Context Detection
3. Services and Mechanisms
Peer Tutoring Collabora. Tasks
Contextual Services
5. Evaluation
Foundations and Learning Theories
Challenge: Resource Selection & Navigation
Challenge: Coopera-tion & Collaboration
Challenge: Feedback & Targeting
Peer Assessment & Feedback Learning
Analytics
Learning Path Transparency
Offline Evaluation
Hypothesis validation
Formative and summative
Resources
Social Patterns
Graph Theory Basics
Scripted Collaboration
Re- com- men- der
Human
Resource User / Learner
KOM – Multimedia Communications Lab 20
“The main issue with designing and maintaining a social web
application is not the technology, it’s the psychology as people and
their activities are the core of the application.”
The quote is no citation of other authors, but written by JK based on [Crumlish et al 2009] and [Bell 2009]
The main issue
KOM – Multimedia Communications Lab 21
Overview on Social System Design Aspects
1. Challenge: Resource Selection
& Navigation
4. Challenge: Cooperation & Collaboration
2. Challenge: Targeting
3. Challenge: Feedback
How to motivate to reach learning goals?
How to design Peer Feedback/Assessment?
How to establish a “community” sense?
How to tell “what’s next”?
Content vs. User
Roles, Levels, Badges, Achievements
Responsibilities and Democracy
Relationship Types
Ambient Intimacy
KOM – Multimedia Communications Lab 22
Publisher-led
Product-led
Interest-Led
Image sources: taken screenshots from each website on 29.10.2014
Structural Patterns
Content vs. User
KOM – Multimedia Communications Lab 23
Publisher-led
Product-led
Interest-Led (hybrids exist)
Image sources of examples: taken screenshots from each website on 29.10.2014
Structural Patterns
Content vs. User
Publisher
Publisher
Interest
Interest
Interest
Product
KOM – Multimedia Communications Lab 24
Content-centric
User-centric
Event-centric
Image sources of examples: taken screenshots from each website on 29.10.2014
Structural Patterns
Content vs. User
KOM – Multimedia Communications Lab 25
Content-centric
User-centric
Event-centric (hybrids exist)
Image sources of examples: taken screenshots from each website on 29.10.2014
Structural Patterns
Content vs. User
Content (Event)
Content Event Content (User)
User (Content)
User
KOM – Multimedia Communications Lab 26
Publisher Product Interest
Content Media Syndication Customer Exchange Learning/Sharing
Event Marketing Franchise Gathering/Exchange
User VIP Promotion Grouping Friendship
Illustration by J.Konert, no specific reference for these dimensions, but see [Bell2009, p. 123ff] for aspects
Structural Patterns
Most interesting for Social Learning and Knowledge Sharing are
Interest-led
Content-centered
Content vs. User
KOM – Multimedia Communications Lab 27
(some relationship arrows are omitted for better readability)
Image source: Tango Icon set,
Relationship Types
Relationship Types
Site owner
Users
Users
Resources
Conversation
Meta-Data Categories Tags Groups
Friendship
Following
Following
Bookmarking
Following
Ownership
Ownership
Sharing
Sharing Following
Ownership
Sharing
Ownership Following
KOM – Multimedia Communications Lab 28
Technically
Symmetric relationships
Discovery of people/groups
Request, Acknowledgement, Decline, Ignore, Remove
Asymmetric relationships
Follow (Fan), Unfollow, Bookmark
Filter
Structure and Content creation
Group creation, deletion, handover ownership
Content creation, deletion, (remaining after account deletion)
Tagging
Privacy and Visibility settings for user-data, content, structures
Search and Recommendation (PULL, PUSH)
Administration (reporting, deletion, reasoning, explanation) [see later slides]
See [Crumlish et al, 2009], p.354-379 for further details. * read http://socialseriousgames.de/post/5437302687/social-serious-gaming-chi-2011-impressions for further details
Relationship Types
Make content and profile creation easy, syndicate and recommend this technically
and allow structure to emerge later on
[cf. Crumlish et al., p378]
Symmetric
Asymmetric
KOM – Multimedia Communications Lab 29
What is reputation?
It’s the general opinion (judgment) (more technically,
a social evaluation) of (and by) the public (or a group or
only a person) towards an entity (person, organization, object or group of entities)
– as distinct and different from the background (others) – concerning the likelihood of the
entity to behave in a certain way in the future [under certain circumstances].
It is a ubiquitous, spontaneous and highly efficient mechanism of social control.
[Crumlish et al. 2009, p. 153], citing Ted Nadeau “Reputation 2.0”
Good.
So let’s give people something that helps for reputation.
Consider
Cooperativeness vs. Competitiveness
Comparability
Quality vs. Quantity
Honor User Loyalty and Progress
Roles, Levels, Badges, Achievements
KOM – Multimedia Communications Lab 30
Named Levels
Reflect the experience (and/or reputation)
Usual measures are
Activities
Likes/Follower
Completion of tasks/quests (if applicable)
(similar, but not ordered, are badges (or labels)..
..given for specific behavior or characteristics..can be extended endlessly)
Honor User Loyalty and Progress
Newbie Active
member Contri- butor
Trend setter Expert Leader Enthusiast
Roles, Levels, Badges, Achievements
KOM – Multimedia Communications Lab 31
Achievements (or Awards)
Reflects accomplished activities
Used to encourage quality
over quantity behavior
Common
Can be reversible
Seldom
Can be unexpected
and hidden
Image source: Konert 2014, .p 67; cf. Konert et al. 2013, Crumlish 2009, p. 166ff
Honor User Loyalty and Progress
Roles, Levels, Badges, Achievements
KOM – Multimedia Communications Lab 32
Image source: https://s3.amazonaws.com/codecademy-blog/assets/intro-new-profile/whole_page.jpg
Example: codecademy.com Profile
Qualitative, single, static Achievements
Badgets for specific skills
Points (as a kind of level)
KOM – Multimedia Communications Lab 33
A Social Web Application should
offer a unique, protected identity
(by email or OpenID etc.)
offer privacy settings
(reasonable defaults, private, protected, public profile and activities)
enforce community guidelines (code of conduct)
grow organically (managed by owner and community)
provide tools for collective governance
(reports, privileges, isolation, timed bans, ..)
allow collaborative filtering (votes, tagging)
never forget that all data belongs to the users
(and that this implies rights to it)
Cf. [Bell, 2009, p. 209-224], [Crumlish et al, 2009, p. 383-397]; image taken from https://info.yahoo.com/legal/sg/yahoo/comms/
Responsibilities
Responsibilities and Democracy
KOM – Multimedia Communications Lab 34
User-generated content administration “Duty of housekeeping”
Easy content creation benefits
Diversification: more variety, specificity, more use/benefits for users
Identification: own content supports emotional binding
Iceberg effect:
Lot of content with low quality
(that should remain under the surface)
..and: illicit content
(18+, NS-symbols, ..)
Solutions:
Youth protection
Content administration
Algorithmic Quality assessment
Responsibilities
Quality of content
Amount
Acceptable quality
Image source: hhttp://www.vertriebslexikon.de/bilder/Eisberg-2009.jpg
Responsibilities and Democracy
KOM – Multimedia Communications Lab 35
Responsibilities
User-generated content administration
A little bit of German law (selection)
Operator is not responsible for law infringement of users
But operator must react promptly, if informed
§10 TMG - Speicherung von Information Diensteanbieter sind für fremde Informationen, die sie für einen Nutzer speichern,
nicht verantwortlich, sofern (1) sie keine Kenntnis von der rechtswidrigen Handlung oder der Information haben und ihnen im
Falle von Schadensersatzansprüchen auch keine Tatsachen oder Umstände bekannt sind, aus denen die rechtswidrige Handlung oder die Information offensichtlich wird, oder
(2) sie unverzüglich tätig geworden sind, um die Information zu entfernen oder den Zugang zu ihr zu sperren, sobald sie diese Kenntnis erlangt haben.
Satz 1 findet keine Anwendung, wenn der Nutzer dem Diensteanbieter untersteht oder von ihm beaufsichtigt wird.
§1004 BGB - Beseitigungs- und Unterlassungsanspruch (1) Wird das Eigentum in anderer Weise als durch Entziehung oder Vorenthaltung des Besitzes
beeinträchtigt, so kann der Eigentümer von dem Störer die Beseitigung der Beeinträchtigung verlangen. Sind weitere Beeinträchtigungen zu besorgen, so kann der Eigentümer auf Unterlassung
klagen. (2) Der Anspruch ist ausgeschlossen, wenn der Eigentümer zur Duldung verpflichtet ist.
Responsibilities and Democracy
KOM – Multimedia Communications Lab 36
Responsibility of Content Administration
User-generated content administration
Setting up prompt reaction and administration of content
Categories of procedures for
administration of UGC*
Algorithm-based
User-based
Operator-based
Requirements to procedures for
administration of UGC*
Correctness of taken decisions
(to delete)
Cost efficiency
Speed of decision taking
in each single case
Amount of content
that can be processed
Complexity of content
that can be processed
*UGC = user-generated content
Responsibilities and Democracy
KOM – Multimedia Communications Lab 38
Responsibility of Content Administration
Operator-based User-based Algorithm-based
Central e.g. SecondLife e.g. Knuddels e.g. Chatsystems
Distributed ? e.g. Wikipedia e.g. P2P Sharing
Responsibilities and Democracy
KOM – Multimedia Communications Lab 39
Responsibility of Content Administration
Operator-Based Responsibility
(Complex-Decision)
User-Based Intermediation
(Fuzzy-Decision)
Algorithm-Based Mass-Processing
(Pre-Decision)
Complexity of content
Amount of Content that can be processed
Examples:
Email complaints
Claim button
Word detection (NLP)
Responsibilities and Democracy
KOM – Multimedia Communications Lab 40
Ambient Intimacy
Key aspect for social learning success
(beside serendipity)
“..is about being able to keep in touch with people with
a level of regularity and intimacy that you wouldn’t
usually have access to, because time and space
conspire to make it impossible.” *
Removing cold ambience and the feeling of being with
others using the application may dramatically increase
app stickiness and in the context of SLKST the
learning success (as it is mainly about self-regulation,
continuity and connecting people by content).
Image sources: own facebook profile feed and video as listed above. * quote from http://www.reboot.dk/page/1236/en ; cf. [Crumlish et al 2009, p.135-152]
Ambient Intimacy
See Video for Interview with Twitter founder Evan Williams of Obvious http://www.technologyreview.com/video/416292/twitter-and-ambient-intimacy/
KOM – Multimedia Communications Lab 43
GRAPH THEORY
1. Theories and Challenges
2. Structures and Pattern
Modeling Context
4. Context-Awareness
Search Context Detection
3. Services and Mechanisms
Peer Tutoring Collabora. Tasks
Contextual Services
5. Evaluation
Foundations and Learning Theories
Challenge: Resource Selection & Navigation
Challenge: Coopera-tion & Collaboration
Challenge: Feedback & Targeting
Peer Assessment & Feedback Learning
Analytics
Learning Path Transparency
Offline Evaluation
Hypothesis validation
Formative and summative
Resources
Social Patterns
Graph Theory Basics
Scripted Collaboration
Re- com- men- der
Human
Resource User / Learner
KOM – Multimedia Communications Lab 45
A Graph G is a pair of sets (Vertexes and Edges)
𝐺 = (𝑉, 𝐸), 𝑉 = 𝑥1, … , 𝑥𝑛 , 𝐸 ⊆ 𝑉 2, 𝑉 ∩ 𝐸 = ∅
Vertexes of a Graph are 𝑉 𝐺 , Edges are 𝐸(𝐺)
Number of vertextes 𝑉 = 𝑛 = |𝐺| is called the order of G
Number of Edges 𝐸 = 𝑚 = 𝐺
Two vertexes 𝑥𝑖 , 𝑥𝑗 ∈ 𝑉(𝐺) are adjacent, if 𝑥𝑖 , 𝑥𝑗 ∈ 𝐸 𝐺 .
Two edges are adjacent if they have an end in common.
The degree 𝑑 𝑥 = |𝐸 𝑥 | of 𝑥 is the number of edges at 𝑥
A path is a non-empty (sub)graph 𝑃 = (𝑉, 𝐸) of the form
𝑉 = 𝑥0, 𝑥1, … , 𝑥𝑘 , 𝐸 = {𝑥0𝑥1, 𝑥1𝑥2, … , 𝑥𝑘−1𝑥𝑘} where 𝑥𝑖 distinct. 𝐸 is the length of P
A tree is a graph where any two vertexes are connected by exactly one unique path
Restrictions: This lecture only treats nontrivial, finite graphs and mostly simple
graphs, i.e. 𝐕 > 𝟎, 𝑮 known and < ∞ , no loops, no double edges
This and following slides are based on [Diestel2006]
Graphs Defined
𝑥1 𝑥2
𝑥3 𝑥4
𝑥5
KOM – Multimedia Communications Lab 46
Vertexes represent users*
Edges represent relations
(friendships) **
Metrics of interest
Which users belong closely to each other?
Which users spread information?
Which users are popular (trendsetting)?
A directed graph (or digraph) 𝐷 = (𝑉, 𝐴) is with
V a finite, nonempty set of vertices and
A a set of ordered pairs of distinct elements of V
called arcs, 𝐴 = {(𝑥𝑖 , 𝑥𝑗)} with 𝑖 ≠ 𝑗, 𝑥𝑖 , 𝑥𝑗 ∈ 𝑉
meaning directed arcs from 𝑥𝑖 to 𝑥𝑗
In-degree 𝑑− 𝑥 of a vertex x is the number of arcs into x.
Out-degree equally defined for out-going arcs from x.
Image source: jscreationzs / FreeDigitalPhotos.net ; [[Oellermann, 2013, p.7]
Social Network Graphs
* Could be as well locations, resources, etc.., but is less common ** could be anything else like “exchanged emails”, “have been at the same spot”, “have a goal in common”. It is very usual to define the edges to the needs of your analysis
KOM – Multimedia Communications Lab 47
Metrics of interest
Which users belong closely to each other?
Which users spread information?
Which users are popular (trendsetting)?
Image source: jscreationzs / FreeDigitalPhotos.net ; [INSNA,2014]
Social Network Graphs
Range (diversity) Number of links to different others (others are defined as different to the extent that they are not
themselves linked to each other, or represent different groups or statuses)
(Tie) strength Amount of time, emotional intensity, intimacy, and reciprocal services (frequency and multiplexity are
also often used as a measure of strength) of a specific link.
Centrality Extent to which an actor is central to a network. Various measures (including degree, closeness, and
betweeness) have been used as indicators of centrality. Some measures of centrality weight an
actor's links to others by the centrality of those others.
Closeness Extent to which an actor is close to, or can easily reach all the other actors in the network. Usually
measured by averaging the path distances (direct and indirect links) to all others. A direct link is
counted as 1, indirect links receive proportionately less weight (e.g. 1/(number of hops)).
Betweeness Extent to which an actor mediates, or falls between any other two actors on the shortest path
between those actors. Usually averaged across all possible pairs in the network.
Prestige Based on asymmetric relationships, prestigious actors are the object rather than the source of
relations. Measures similar to centrality are calculated by accounting for the direction of the
relationship (i.e. in-degree). Prestige can then be defined e.g. as in-degree / out-degree
KOM – Multimedia Communications Lab 48
Representation
Adjacency matrix
For our finite simple graphs a adjacency matrix is a matrix
with zeros on its diagonal and ones (1) for each edge
connecting 𝑥𝑖 and 𝑥𝑗. The matrix is always symmetric if the graph is undirected.
A =
01010
10110
01011
11100
00100
, algorithmically you store an 2-dimensional array A[i][j].
Adjacency list
Storage of all neighbors of the vertexes as a list, e.g.
𝐴 = 𝑥2, 𝑥4 , 𝑥1, 𝑥3, 𝑥4 , 𝑥2, 𝑥4, 𝑥5 , 𝑥1, 𝑥2, 𝑥3 , 𝑥3 ,
algorithmically you store as well a 2-dimensional array
Which way is more efficient?
Depends on sparsity and operations
(Social Network) Graphs
𝑥1 𝑥2
𝑥3 𝑥4
𝑥5
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A graph 𝑮 = (𝑽, 𝑬) is called n-partite if V admits a partition into r classes such that
every edge has its ends in different classes. 2-partite is usually called bipartite.
A hypergraph is a generalization of a graph with 𝐸 ⊆ 𝑃 𝑉 \ ∅
In a k-uniform hypergraph all hyperedges have size k.
Thus an k-uniform k-partite hypergraph consists of edges connecting k-tupels of
vertexes that all belong to k disjunct sets.
For hypergraphs see as well http://en.wikipedia.org/wiki/Hypergraph#Bipartite_graph_model
N-partite graphs and k-uniform hypergraphs
𝑥1
𝑥2
𝑥3
𝑥4
𝑥5
𝑥6 𝑥7
bipartite 3-partite
3-partite
3-uniform
𝐻 = 𝑉, 𝐸 , 𝑉 = 𝑉𝑏 ∩ 𝑉𝑔 ∩ 𝑉𝑟 = 𝑥1, 𝑥2, … , 𝑥7 , 𝐸 = { 𝑥1𝑥4𝑥6 , 𝑥2𝑥5𝑥6 , 𝑥2𝑥5𝑥7 , 𝑥3𝑥4𝑥7 }
KOM – Multimedia Communications Lab 50
How to weight the edges in an k-uniform, n-partite graph to
recommend other related vertexes of a disjunctive set?
How to calculate a betweenness centrality of resources in such n-
partite graphs with users, resources and tags?
Emerging aspects
See lectures 8 and 9 on recommender Systems in SLKST context
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Approaches to Modern
Web Application Development
MVC, ACID, CRUD REST,
LAMP, MEAN, PaaS
Social Media Systems Design Aspects
Graph Theory Basics
Centrality metrics of (un)directed graphs
N-partite, k-uniform hypergraphs
Image sources: http://www.seawaterfoundation.org/siteImages/rivers_art.jpg,, http://vnfa8y5n3zndutm1.zippykid.netdna-cdn.com/wp-content/uploads/2011/12/url7.jpg, http://images.all-free-
download.com/images/graphiclarge/s_bahn_71263.jpg, http://de.roblox.com/item.aspx?seoname=U-Bahn&id=28172595, http://faculty.kutztown.edu/rieksts/225/graphs/tripartite_files/image002.jpg,
Summary
Content vs. User Roles, Levels, Badges, Achievements Relationship Types
𝑥1
𝑥2
𝑥3
𝑥4
𝑥5
𝑥6 𝑥7
Adjacence list
𝐴 = 𝑥2, 𝑥3 , 𝑥1, 𝑥3, 𝑥4 , 𝑥2, 𝑥4, 𝑥5 , 𝑥1, 𝑥2, 𝑥3 , 𝑥3
Responsibilities and Democracy
Ambient Intimacy
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Task1 (2p)
Designing the relation of system design aspects and 3 systems as a graph
(connecting social System Design Patterns with Graph Theory)
Task 2 (2p)
A new (fictive) SLKS system is described
Define and describe 6 types of relationships
How would you implement a reputation/progress system if you have to choose
one pattern?
How do you handle content administration?
A bonus challenge is included (task 1)
About the Exercise 3
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Learner Models & Profiles
Learning Resources
Data Structures for Learning Content
Metadata to describe Learning Resources
Tags to describe Learning Resources
Next week: Lecture 4
Data Structures for Learner and Resources
Knowledge
Topics
Misconceptions
Learning Styles
Experience
…
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Thank you for your attention, questions, feedback or hints.
Endslide
[email protected]…. .de [email protected]…. .de
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REST, see JAX-RS specs and https://jersey.java.net/documentation/latest/getting-started.html
Crumlish, C.; Malone, E.: Designing Social Interfaces: Principles, Patterns, and Practices for Improving the User
Experience (Animal Guide) (p. 520). Sebastopol, USA: O’Reilly Media, 2009. Letzter Zugriff von
http://www.amazon.com/Designing-Social-Interfaces-Principles-Experience/dp/0596154925
Bell, G.: Building Social Web Applications. Bell, Gavin . Sebastopol: O’Reilly Books, 2009. Letzter Zugriff 29.10.2014 von
http://www.amazon.com/Building-Social-Applications-Gavin-Bell/dp/0596518757
Konert, J.; Gerwien, N.; Göbel, S.; Steinmetz, R. Bringing Game Achievements and Community Achievements Together.
In Proceedings of the 7th European Conference on Game Based Learning (ECGBL) 2013, pages 319–328, Porto
Portugal, 2013. Academic Publishing International. ISBN 978-1- 909507-63-0.
Konert, J.: Interactive Multimedia Learning: Using Social Media for Peer Education in Single-Player Educational Games
(p. 220). Darmstadt, Germany: Springer, 2014. Letzter Zugriff von
http://www.springer.com/engineering/signals/book/978-3-319-10255-9
Diestel, R.: Graph Theory (Graduate Texts in Mathematics) (3rd ed.). Springer, 2006. Letzter Zugriff 30.10.2014 von
http://www.amazon.de/Graph-Theory-Graduate-Texts-Mathematics/dp/3540261834/
Oellermann, O. R.: Topics in Structural Graph Theory. (L. W. Beinecke & R. J. Wilson, Hrsg.). Cambridge, MA, USA:
Cambridge University Press, 2013.
INSNA, International Network for Social Network Analysis, 2004. SNA Measures. , p.1. Available at:
https://www.socialtext.net/data/workspaces/insna-
socnet/attachments/index_of_sna_measures:20041202193540/original/Index of SNA Measures.xls.
Wikipedia, Hypergraphs, Letzer Zugriff 30.10.2014 von http://en.wikipedia.org/wiki/Hypergraph#Bipartite_graph_model
References (order of occurance)
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REST, see JAX-RS specs and https://jersey.java.net/documentation/latest/getting-started.html
Crumlish, C.; Malone, E.: Designing Social Interfaces: Principles, Patterns, and Practices for Improving the User
Experience (Animal Guide) (p. 520). Sebastopol, USA: O’Reilly Media, 2009. Letzter Zugriff von
http://www.amazon.com/Designing-Social-Interfaces-Principles-Experience/dp/0596154925
Bell, G.: Building Social Web Applications. Bell, Gavin . Sebastopol: O’Reilly Books, 2009. Letzter Zugriff 29.10.2014 von
http://www.amazon.com/Building-Social-Applications-Gavin-Bell/dp/0596518757
Diestel, R.: Graph Theory (Graduate Texts in Mathematics) (3rd ed.). Springer, 2006. Letzter Zugriff 30.10.2014 von
http://www.amazon.de/Graph-Theory-Graduate-Texts-Mathematics/dp/3540261834/
Further Readings
KOM – Multimedia Communications Lab 57
Directly named on the corresponding slides
Image Sources