Presentation on applied social network analysis for data scientists.

1. M E T I S M E E T U P
Networks All Around Us: Analyzing Networks in your Problem Domain | 3/3/2016
Russell Jurney
http://bit.ly/socialnetworkanalysis2

2. RELATO
MAPS
MARKET

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

10. MARKET
REPORTS

11. TYPES OF NETWORKS

12. TINKERPOP
“Marko Rodriguez is the Doug Cutting of graph analytics.”
—Mark Twain

13. PROPERTY
GRAPHS

14. A PROPERTY
GRAPH IN
EVERY
DATABASE

15. PROPERTY GRAPHS IN YOUR DOMAIN
identify entities
identify relationships
specify schema (or not)
populate graph database
learn to think in graph walks (hard)
query in batch
query in realtime

16. 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)
}

17. 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)
}

18. MULTI
RELATIONAL
TO SINGLE
RELATIONAL
g.E(‘friend’).subgraph()

19. final Graph g = TinkerFactory.createClassic();
try (final OutputStream os = new FileOutputStream(“jsondump/links.json")) {
GraphSONWriter.build().create().writeGraph(os, g);
}
EXPORT LINKS AS JSON

20. THEN USE
SNA
LIBRARIES
#
# Example - calculate friendship dispersion
#
di_graph = nx.DiGraph()
all_edges = util.json_cr_file_2_array('jsondump/links.json')
for edge in all_edges:
if 'type' in edge and edge['type'] == 'partnership':
di_graph.add_edge(edge['domain1'], edge[‘domain2'])
dispersion = nx.dispersion(di_graph)

21. TOOLS OF
SNA
SNA = Social Network Analysis
centrality
clustering
block models
cores
dispersion
center-pieces

22. 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?

23. 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()

24. 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()

25. CENTRALITIES
degree centrality
closeness centrality
betweenness centrality
eigenvector centrality

26. DEGREE CENTRALITY
in-degree centrality is nice…
it works even if you’re missing
a node’s outbound links

27. DEGREE CENTRALITY
# computation
count connections
…its that simple
in-degree centrality = popularity
out-degree centrality = gregariousness
# meaning
risk of catching cold

28. DEGREE CENTRALITY IN GREMLIN
# all-links-the-same-type-centrality
g.V().out().groupCount()

29. CLOSENESS CENTRALITY
# computation
count hops of all shortest paths
distance from all other nodes
reciprocal of farness
# meaning
communication efficiency
spread of information

30. CLOSENESS CENTRALITY IN GREMLIN
closenessCentrality =
g.V().as(“a”).repeat(both(‘relationship_type').simplePath()).emit().as("b")
.dedup().by(select(“a","b")).path()
.group().by(limit(local, 1)).by(count(local)
.map {1/it.get()}.sum())

31. BETWEENNESS CENTRALITY
# computation
count of times node appears in shortest paths…
…between all pairs of nodes
# meaning
control of communication between other nodes

32. 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

33. EIGENVECTOR CENTRALITY IN GREMLIN
g.V()
.repeat(out(‘relationship_type’).groupCount(‘m').by('unique_key'))
.times(n).cap('m')

34. CLUSTERING

35. CLUSTERING
property based clustering: k-means
graph based clustering: modularity
property graph based clustering: CESNA

36. BLOCK MODELS
how much do clusters
connect?
are links reciprocal?
circos are helpful

37. CORES

38. DISPERSION
Romantic Partnerships and the Dispersion of Social Ties:
A Network Analysis of Relationship Status on Facebook

39. CENTER-PIECE SUBGRAPHS
*Slide stolen from Tong, Faloutsos, Pan

40. Russell Jurney, CEO
rjurney@relato.io
twi?er.com/rjurney
404-317-3620
http://bit.ly/socialnetworkanalysis2