Networks All Around Us: Extracting networks from your problem domain
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Network analytics are being increasingly utilized to create machine intelligence that automates the world around us. But what is a network, and how do you analyze them? More directly: how do I find and analyze networks in my dataset? This talk will go over a number of examples of practical network analytics to give viewers a playbook for doing applied social network analysis and network analytics.
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Networks All Around Us: Extracting networks from your problem domain
- 1. DATA DAY T E X A S Networks All Around Us: Discovering Networks in your Domain | 1/5/2015 Russell Jurney
- 3. BACKGROUND Serial Entrepreneur Contributed code to Apache Druid, Apache Pig, Apache DataFu, Apache Whirr, Azkaban, MongoDB Apache Commi?er Three-Bme O'Reilly Author Started & Shipped Product at E8 Security Ning, LinkedIn, Hortonworks veteran
- 4. 2009 2010 2011 2012 2014 EXAMPLES OF NETWORKS
- 5. FOUNDER NETWORKS node = company edge = employment transition as in people who… …worked at one startup, founded another
- 6. WEBSITE BEHAVIOR node = web page edge = user browses one page, then another
- 7. ONLINE SOCIAL NETWORKS node = linkedin profile, edge = linked connection
- 8. EMAIL INBOX node = email address, edge = sent email
- 9. MARKETS node = company, edge = partnership
- 11. TINKERPOP “Marko Rodriguez is the Doug Cutting of graph analytics.” —Mark Twain
- 12. PROPERTY GRAPHS
- 14. PROPERTY GRAPHS IN YOUR DOMAIN identify entities identify relationships specify schema (or not) populate graph database learn to think in graph walks query in batch query in realtime
- 15. POPULATING A PROPERTY GRAPH // Add nodes while((json = company_reader.readLine()) != null) { document = jsonSlurper.parseText(json) v = graph.addVertex('company') v.property("_id", document._id) v.property("domain", document.domain) v.property("name", document.name) }
- 16. POPULATING A PROPERTY GRAPH // Get a graph traverser g = graph.traversal() while((json = links_reader.readLine()) != null) { document = jsonSlurper.parseText(json) // Add edges to graph v1 = g.V().has('domain', document.home_domain).next() v2 = g.V().has('domain', document.link_domain).next() v1.addEdge(document.type, v2) }
- 17. TOOLS OF SNA SNA = Social Network Analysis centrality clustering block models cores dispersion
- 18. CENTRALITY Centrality is a way of measuring how central or important a particular node is in a social network. OR What nodes should I care about?
- 19. SINGLE-RELATIONAL CENTRALITY(S) # all-links-the-same-type-centrality g.V().out().groupCount() # things-humans-walk-centrality g.V().hasLabel(‘human’).out(‘walks’).groupCount() # things-dogs-eat-centrality g.V().hasLabel(‘dog’).out(‘eats’).groupCount()
- 20. MULTI-RELATIONAL CENTRALITY(S) # things-eaten-by-things-humans-walk-centrality g.V().hasLabel(‘human’).out(‘walks’).out(‘eats’).groupCount() # things-hated-by-things-humans-pet-centrality g.V().hasLabel(‘human’).out(‘pets’).out(‘hates’).groupCount() # things-that-pet-things-that-eat-mice-centrality g.V().in(‘eats’).in(‘pets’).groupCount()
- 21. CENTRALITIES degree centrality closeness centrality betweenness centrality eigenvector centrality
- 22. DEGREE CENTRALITY in-degree centrality is nice… it works even if you’re missing a node’s outbound links
- 23. DEGREE CENTRALITY # computation count connections …its that simple in-degree centrality = popularity out-degree centrality = gregariousness # meaning risk of catching cold
- 24. CLOSENESS CENTRALITY # computation count hops of all shortest paths distance from all other nodes reciprocal of farness # meaning communication efficiency spread of information
- 25. BETWEENNESS CENTRALITY # computation count of times node appears in shortest paths… …between all pairs of nodes # meaning control of communication between other nodes
- 26. EIGENVECTOR CENTRALITY # computation counts connections of connected nodes more connected neighbors matter more # meaning influence of one node on others pagerank is an eigenvector centrality
- 27. CLUSTERING
- 28. CLUSTERING property based clustering: k-means graph based clustering: modularity property graph based clustering: CESNA
- 29. BLOCK MODELS how much do clusters connect? are links reciprocal? circos are helpful
- 30. CORES
- 31. DISPERSION Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook