Modularity-Driven Clustering of Dynamic Graphs

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Modularity-Driven Clustering of Dynamic Graphs
Maximizing the quality index modularity has become one of the primary methods for identifying the clustering structure within a graph. As contemporary networks are not static but evolve over time, traditional static approaches can be inappropriate for specific tasks. In this work we pioneer the NP-hard problem of online dynamic modularity maximization. We develop scalable dynamizations of the currently fastest and the most widespread static heuristics and engineer a heuristic dynamization of an optimal static algorithm. Our algorithms efficiently maintain a modularity-based clustering of a graph for which dynamic changes arrive as a stream. For our quickest heuristic we prove a tight bound on its number of operations. In an experimental evaluation on both a real-world dynamic network and on dynamic clustered random graphs, we show that the dynamic maintenance of a clustering of a changing graph yields higher modularity than recomputation, guarantees much smoother clustering dynamics a...
Robert Görke, Pascal Maillard, Christian Stau
Added 09 Jul 2010
Updated 09 Jul 2010
Type Conference
Year 2010
Where WEA
Authors Robert Görke, Pascal Maillard, Christian Staudt, Dorothea Wagner
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