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Analysis of streaming social networks and graphs on multicore architectures

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5 Author(s)
Riedy, J. ; Georgia Inst. of Technol., Atlanta, GA, USA ; Meyerhenke, H. ; Bader, D.A. ; Ediger, D.
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Analyzing static snapshots of massive, graph-structured data cannot keep pace with the growth of social networks, financial transactions, and other valuable data sources. We introduce a framework, STING (Spatio-Temporal Interaction Networks and Graphs), and evaluate its performance on multicore, multisocket Intel®-based platforms. STING achieves rates of around 100 000 edge updates per second on large, dynamic graphs with a single, general data structure. We achieve speedups of up to 1000× over parallel static computation, improve monitoring a dynamic graph's connected components, and show an exact algorithm for maintaining local clustering coefficients performs better on Intel-based platforms than our earlier approximate algorithm.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012