Social networks produce an enormous quantity
of data. Facebook consists of over 400 million active
users sharing over 5 billion pieces of information
each month. Analyzing this vast quantity of unstructured
data presents challenges for software and hardware. We
present GraphCT, a Graph Characterization Toolkit for
massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness
centrality of an artificially generated (R-MAT) 537 million
vertex, 8.6 billion edge graph in 55 minutes and a realworld
graph (Kwak, et al.) with 61.6 million vertices
and 1.47 billion edges in 105 minutes. We use GraphCT
to analyze public data from Twitter, a microblogging
network. Twitter's message connections appear primarily
tree-structured as a news dissemination system. Within the
public data, however, are clusters of conversations. Using
GraphCT, we can rank actors within these conversations
and help analysts focus attention on a much smaller data
subset.
