A Dynamic Algorithm for Local Community Detection in Graphs

3 years 9 months ago
A Dynamic Algorithm for Local Community Detection in Graphs
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 is constantly produced, and vertices and edges are inserted and removed from a graph. We present an algorithm for dynamic seed set expansion, which incrementally updates the community 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. The dynamic approach also improves performance compared to recomputation, achievi...
Anita Zakrzewska, David A. Bader
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Authors Anita Zakrzewska, David A. Bader
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