Community and cluster detection is a popular field of social network analysis. Most algorithms focus on static graphs or series of snapshots.

In this paper we present an algorithm, which detects communities in dynamic graphs. The method is based on the shortest paths to high-connected nodes, so called hubs. Due to local message passing, we can update the clustering results with low computational effort.

The presented algorithm is compared with the Louvain method on large-scale real-world datasets with given community structure. The detected community structure is compared to the given with NMI scores. The advantage of the algorithm is the good performance in dynamic scenarios.


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Computer ScienceOtto von Guericke University of MagdeburgMagdeburgGermany

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