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Networks All Around Us: Extracting networks from your problem domain
The document discusses various types of networks and their properties, emphasizing the use of property graphs for social network analysis (SNA). It covers the concept of centrality, different centrality measures, and methods for populating and querying graph databases. Additionally, it includes practical examples of how to analyze relationships and behaviors within networks.
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Networks All Around Us: Extracting networks from your problem domain
- 1. DATA DAY TE X A S Networks All Around Us: Discovering Networks in your Domain | 1/5/2015 Russell Jurney
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- 3. BACKGROUND Serial Entrepreneur Contributedcode 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 20122014 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 = webpage edge = user browses one page, then another
- 7. ONLINE SOCIAL NETWORKS node = linkedinprofile, edge = linked connection
- 8. EMAIL INBOX node = emailaddress, edge = sent email
- 9. MARKETS node = company,edge = partnership
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- 11. TINKERPOP “Marko Rodriguez isthe Doug Cutting of graph analytics.” —Mark Twain
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- 14. PROPERTY GRAPHS INYOUR 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 PROPERTYGRAPH // 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 PROPERTYGRAPH // 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 away 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 betweennesscentrality eigenvector centrality
- 22. DEGREE CENTRALITY in-degree centralityis nice… it works even if you’re missing a node’s outbound links
- 23. DEGREE CENTRALITY # computation countconnections …its that simple in-degree centrality = popularity out-degree centrality = gregariousness # meaning risk of catching cold
- 24. CLOSENESS CENTRALITY # computation counthops of all shortest paths distance from all other nodes reciprocal of farness # meaning communication efficiency spread of information
- 25. BETWEENNESS CENTRALITY # computation countof times node appears in shortest paths… …between all pairs of nodes # meaning control of communication between other nodes
- 26. EIGENVECTOR CENTRALITY # computation countsconnections of connected nodes more connected neighbors matter more # meaning influence of one node on others pagerank is an eigenvector centrality
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- 28. CLUSTERING property based clustering:k-means graph based clustering: modularity property graph based clustering: CESNA
- 29. BLOCK MODELS how muchdo clusters connect? are links reciprocal? circos are helpful
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- 31. DISPERSION Romantic Partnerships andthe Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook
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