Overlapping Community Detection by Online Cluster Aggregation
Mark Kozdoba, Shie Mannor
TL;DR
This paper introduces an online algorithm for detecting overlapping communities in large graphs, combining a modified online k-means approach with a novel overlap modeling technique, achieving high-quality results efficiently.
Contribution
It proposes a new online clustering algorithm specifically designed for overlapping community detection, with improved speed and comparable or better quality than existing methods.
Findings
High-quality community detection on large benchmark graphs
Significantly improved running time over existing methods
Effective modeling of community overlaps
Abstract
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.
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Complex Network Analysis Techniques · Spam and Phishing Detection