Massive social network analysis: Mining twitter for social good

David Ediger, Karl Jiang, Jason Riedy, David A. Bader, Courtney Corley, Rob Farber, William N. Reynolds

Research output: Chapter in Book/Report/Conference proceedingConference contribution

128 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 39th International Conference on Parallel Processing, ICPP 2010
Pages583-593
Number of pages11
DOIs
StatePublished - 2010
Externally publishedYes
Event39th International Conference on Parallel Processing, ICPP 2010 - San Diego, CA, United States
Duration: Sep 13 2010Sep 16 2010

Publication series

NameProceedings of the International Conference on Parallel Processing
ISSN (Print)0190-3918

Conference

Conference39th International Conference on Parallel Processing, ICPP 2010
Country/TerritoryUnited States
CitySan Diego, CA
Period9/13/109/16/10

All Science Journal Classification (ASJC) codes

  • Software
  • General Mathematics
  • Hardware and Architecture

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