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. © 2010 IEEE.
CITATION STYLE
Ediger, D., Jiang, K., Riedy, J., Bader, D. A., Corley, C., Farber, R., & Reynolds, W. N. (2010). Massive social network analysis: Mining twitter for social good. In Proceedings of the International Conference on Parallel Processing (pp. 583–593). https://doi.org/10.1109/ICPP.2010.66
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