Graph Analytics in Social Networks
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Overview of graph analytics in social networks. Social Network Analytics.
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Graph Analytics in Social Networks
- 1. RED/082311 Keen Analytics Social Behavior and Graph Analytics Arthur Keen, PhD
- 2. RED/082311 Keen Analytics Agenda • What is a graph? • The connection between behavior and graphs • What can graphs tell us about behavior? • Interactive Graph Visualizations 2
- 3. RED/082311 Keen Analytics What is a graph? 3 This is a graph. This is not a graph.
- 4. RED/082311 Keen Analytics What is a Graph? 4 1 2 3 4 5 0 Vertex/Node Edge Cycle Self Edge Articulation Point Degree = 2 In Degree = 0 Out Degree = 2 Degree = 4 In Degree = 3 Out Degree = 1 Degree = 1 In Degree = 1 Out Degree = 0 Degree = 5 In Degree = 2 Out Degree = 3
- 5. RED/082311 Keen Analytics Behavior and Graphs 5 As we interact with the world around us, we experience physical, mental, and virtual connections. Every smile, touch, click, email, joke, and IM makes connections. Connections may last an instant, a lifetime, or multiple life times. Collectively, these connections form a graph of all interactions. Social media, email, blogs, etc., form part of this graph
- 6. RED/082311 Keen Analytics How to make sense of graphs How are events connected? What are the communities? What is unusual? (Anomaly Detection) Who is important/influential? Are two patterns the same? (Isomorphism) What can be inferred? (Semantics/Inference) 6
- 8. RED/082311 Keen Analytics Graph Aggregation and Visualization 8 Imagine the graph of all the recipes Understand How Recipes Differ By Region
- 9. RED/082311 Keen Analytics Understand and Predict Group Behavior 9 Execute Plan Transition Execute
- 10. RED/082311 Keen Analytics Divided We Blog 10
- 11. RED/082311 Keen Analytics Language and Communication 11
- 12. RED/082311 Keen Analytics Interactive Graph Vizualization • Political Influence Visualization • http://www.brightpointinc.com/interactive/political_influence/index.html?source=d3js • Les Miserables Graph Layout • http://nylen.tv/d3-process-map/graph.php?dataset=les-mis • http://visjs.org/examples/network/exampleApplications/lesMiserables.html • Les Miserables Identifying groups • http://bost.ocks.org/mike/miserables/ • Graph Layout with images • http://visjs.org/examples/network/nodeStyles/circularImages.html 12
- 14. RED/082311 Keen Analytics Les Miserables Graph 14 http://nylen.tv/d3-process-map/graph.php?dataset=les-mis
- 15. RED/082311 Keen Analytics Co-Occurrence (groups) in Les Miserables 15http://bost.ocks.org/mike/miserables/
- 16. RED/082311 Keen Analytics Resources 16 • Graph Databases • Neo4j (http://neo4j.com) • StarDog (http://stardog.com) • OntoText GraphDB (http://ontotext.com) • AllegroGraph (http://allegrograph.com) • Graph Analytics • Dato GraphLab (http://dato.com) • Spark GraphX (https://spark.apache.org/graphx/) • iGraph (http://igraph.org) • Graph Visualization • Gephi (http://gephi.org) • Visjs (http://visjs.org) • KeyLines (http://keylines.com) • D3js (http://d3js.org) • Sigma (http://sigmajs.org) • Linkurious (http://linkurio.us)
- 19. RED/082311 Keen Analytics Compute Social Graph Density With SPARQL 19 This kind of query ‘touches’ the entire graph
- 20. RED/082311 Keen Analytics Example: Detect Abnormal Behavior Degree Centrality = 3 (Vertex edge count) Probability Degree Centrality 20 Rare Occurrence (Frequent Communication) Rare Occurrence (Infrequent Communication) Normal Communication Levels
Editor's Notes
- This is a slide build showing the visualized results of: Centrality analysis: To identify the most central entities in your network, a very useful capability for influencer marketing. Path analysis: To identify all the connections between a pair of entities, useful in understanding risks and exposure. Community detection: To identify clusters or communities, which is of great importance to understanding issues in sociology and biology. Sub-graph isomorphism: To search for a pattern of relationships, useful for validating hypotheses and classifying subgraphs Anomaly Detection: Detect and characterize unusual patterns of behavior to search for abnormal situations, such as hacker attacks or fraud Behavior Prediction: Graphs represent behavior over time and contain behavior patterns. Identifying partial patterns enables behavior prediction.
- Visualization of Social Graph Density. Briefly describe what Social Density could be used for The following slide shows how the was created
- This slide portrays how the Social Graph Visual in the previous slide was created
- Visualization of Centrality Distribution. Briefly Describe what Centrality Distribution could be used for The following slide shows how the was created