David A. Bader
IEEE Fellow
AAAS Fellow
College of Computing
Georgia Tech
Atlanta, GA 30332



Massive Social Network Analysis: Mining Twitter for Social Good

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.

Publication History

Versions of this paper appeared as:
  1. David Ediger, Karl Jiang, Jason Riedy, David A. Bader, Courtney Corley, Rob Farber and William N. Reynolds. ``Massive Social Network Analysis: Mining Twitter for Social Good,'' The 39th International Conference on Parallel Processing (ICPP 2010), San Diego, CA, September 13-16, 2010.

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Last updated: October 1, 2010


Computational Biology

Parallel Computing