Social Network Mining
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- Hortense Wilkinson
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1
2 Social Network Mining Data Mining November 11, 2013 Frank Takes LIACS, Universiteit Leiden
3 Overview Social Network Analysis Graph Mining Online Social Networks Friendship Graph Semantics Example Conclusions
4 Social Network Analysis Social Network Analysis: the study of social networks to understand their structure and behavior. Social Network: a social structure of people, related (directly or indirectly) to each other through a common relation or interest. Social Networks!= Social Media
5 Social Network Analysis Social Network Analysis (SNA) Sociology Algorithms Data Mining Social Networks Real-life (explicit) Online (explicit) Derived (implicit) networks, citation networks, co-author networks, terrorist collaboration networks
6 SNA Research Topics Network analysis Measuring Modelling Prediction Spread of Information Trust & Authority Community Detection Semantics Anonimity & Privacy
7 Graph Mining
8 Graphs n = 5 nodes m = 6 edges A Vertex/Node C Distance d(c, E) = d(e, C) = 2 D E Relationship/Edge/Link
9 Data / Graph Mining Data Mining: looking for patterns in Databases id name date_of_birth 23. Stinson [email protected] 42 T. Mosby [email protected] classification, clustering, outlier detection, Graph Mining: looking for patterns in Graphs A E D So what are we looking for? C
10 Graph Mining Graph models Centrality measures (local) Graph properties (global) Frequent subgraphs (pattern mining) Detecting cliques (clustering) Link prediction (classification) Various application domains
11 Centrality measures Degree centrality etweenness centrality Closeness centrality Graph centrality (eccentricity centrality) Eigenvector centrality Random walk centrality Hyperlink Induced Topic Search (HITS) PageRank
12 Centrality Who has a central position in this graph? A E F G H A E F G H D D C C Degree Centrality: C has the highest degree
13 Centrality Who has a central position in this graph? A E F G H A E F G H D D C C etweenness Centrality: E is part of the largest number of shortest paths
14 Google PageRank
15 Google PageRank 4 webpages A,, C and D (N = 4) Initially: PR(A) = PR() = PR(C) = PR(D) = 1/n L(A) is the outdegree of page A Now if, C and D each link to A, the simple PageRank PR(A) of a page A is equal to:
16 Google PageRank A A C D C D
17 Google PageRank PageRank as suggested by Larry Page in 1999 N = number of pages, p i and p j are pages M(p i ) is the set of pages linking to p i L(p j ) is the outdegree of p j d = 0.85, 85% chance to follow a link, 15% chance to jump to a random page (random surfer) t = 0 t = t + 1
18 Frequent Subgraphs What pattern occurs frequently in this graph? A A C A A A A A A C C C Frequent Subgraph: A--C
19 Clustering
20
21 Online Social Networks
22 History 1997: SixDegrees.com 2000: Friendster 2003: LinkedIn & MySpace 2004: Hyves 2005: Facebook 2006: Twitter : The Social Network (movie)
23 Online Social Networks User (node) has a profile Profiles have attributes (labels/annotations) Explicit links (edges) Social Links / Friendship links User groups Implicit links Social messaging Common attributes Directed vs. undirected links
24 2011
25
26 Example: Facebook More than 1 billion active users Average user has 130 friends Estimated 100 billion social links Over 600 million interactive objects (pages, groups and events) More than 45 billion pieces of content (web links, news stories, blog posts, notes, photo albums, etc.) shared each month
27 OSNA Research Topics User behavior Privacy & Anonymity Trust & Authorities Diffusion of information Sampling & Crawling Community Detection Friendship Graph
28 Friendship Graph
29 Friendship Graph
30 Friendship Graph Analysis Static analysis Densely connected core Fringe of low-degree nodes Few isolated communities & singletons Static properties Node degree distribution, average distance, diameter Edge/node ratio, level of symmetry Number of cliques, k-cliques, etc. Small world phenomanon
31 Static properties
32 Degree Distribution
33 Distance Distribution
34 Small World Networks Class of networks with certain properties: Sparse graphs Highly connected Short average node-to-node distance: d ~ log(n) Fat tailed power law node degree distribution Densely connected core with many (near-)cliques Existence of hubs: nodes with a very high degree Fringe of low(er)-degree nodes
35 Regular vs. Small World
36 Small World Networks Other examples of small world networks Web graphs Gene networks networks Telephone call graphs Information networks Internet topology networks Scientific co-authorship networks Corporate networks (interlocks or ownerships)
37 Friendship Graph Analysis Static analysis Dynamic analysis Network evolution Network modelling Network growth Link Prediction Triadic Closure Preferential Attachment Semantic Link Prediction
38 Link Prediction A E A E D D C C Two principles: preferential attachment and triadic closure
39 Triadic Closure A C A C Two principles: preferential attachment and triadic closure
40 Preferential Attachment Nodes with a large degree acquire new links at a faster rate. A E F J G C D
41 Semantics
42 "Call for Semantics" Structured data volumes grow rapidly "Semantic Web"
43 "Call for Semantics" What is the meaning of a link? What type of relation is defined by a link? Are there any wrong links? What is the strength of a link? Are link descriptions missing?
44 Semantic Link Prediction Deterministic sibling E sibling E? uncle parent? parent parent uncle parent C? D C cousin D
45 Semantic Link Prediction Predictive (learning, classification, etc.) colleague E colleague E? colleague colleague? colleague colleague colleague colleague C colleague D C colleague D
46 Conclusions Online social networks are an excellent domain of study for data (or graph-) miners Social Network Analysis is important for many areas of research, not only computer science Semantics within large networks are becoming increasingly more important Challenges may be found in the temporal (dynamic) analysis of social networks
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