Massive social network analysis: Mining twitter for social good

127Citations
Citations of this article
330Readers
Mendeley users who have this article in their library.
Get full text

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. © 2010 IEEE.

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free