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Social Network Analysis in Your Problem Domain
1. G R A P H DAY T E X A S TA L K
Networks All Around Us: Discovering Networks in your Domain | 1/5/2015
Russell Jurney
http://bit.ly/socialnetworkanalysis
15. final Graph g = TinkerFactory.createClassic();
try (final OutputStream os = new FileOutputStream(“jsondump/links.json")) {
GraphSONWriter.build().create().writeGraph(os, g);
}
EXPORT LINKS AS JSON
16. 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)
18. 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
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
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')