Overlapping Community Detection by Online Cluster Aggregation
This addresses the problem of efficiently detecting overlapping communities in networks, which is incremental as it builds on existing methods with runtime improvements.
The paper tackles overlapping community detection by introducing an online algorithm that modifies online k-means and models overlap, resulting in competitive community quality and significantly improved running time on large benchmark graphs.
We present a new online algorithm for detecting overlapping communities. The main ingredients are a modification of an online k-means algorithm and a new approach to modelling overlap in communities. An evaluation on large benchmark graphs shows that the quality of discovered communities compares favorably to several methods in the recent literature, while the running time is significantly improved.