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Tracking local communities in streaming graphs with a dynamic algorithm

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Abstract

A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which finds the best local community for a given set of seed vertices. Greedy, agglomerative algorithms, which are commonly used in seed set expansion, have been previously designed only for a static, unchanging graph. However, in many applications, new data are constantly produced, and vertices and edges are inserted and removed from a graph. We present an algorithm for dynamic seed set expansion, which maintains a local community over time by incrementally updating as the underlying graph changes. We show that our dynamic algorithm outputs high-quality communities that are similar to those found when using a standard static algorithm. It works well both when beginning with an already existing graph and in the fully streaming case when starting with no data. The dynamic approach is also faster than re-computation when low latency updates are needed.

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Acknowledgments

The work depicted in this paper was partially sponsored by Defense Advanced Research Projects Agency (DARPA) under agreement #HR0011-13-2-0001 (DARPA PERFECT). The content, views and conclusions presented in this document do not necessarily reflect the position or the policy of DARPA or the U.S. Government, no official endorsement should be inferred.

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  1. Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Anita Zakrzewska & David A. Bader

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  1. Anita Zakrzewska
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  2. David A. Bader
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Correspondence to Anita Zakrzewska.

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Zakrzewska, A., Bader, D.A. Tracking local communities in streaming graphs with a dynamic algorithm. Soc. Netw. Anal. Min. 6, 65 (2016). https://doi.org/10.1007/s13278-016-0374-5

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