Quadratic Optimization based Clique Expansion for Overlapping Community Detection
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by
Yanhao Yang, Pan Shi, Yuyi Wang, Kun He
2020
Abstract
Community detection is crucial for analyzing social and biological networks,
and comprehensive approaches have been proposed in the last two decades.
Nevertheless, finding all overlapping communities in large networks that could
accurately approximate the ground-truth communities remains challenging. In
this work, we present the QOCE (Quadratic Optimization based Clique Expansion),
an overlapping community detection algorithm that could scale to large networks
with hundreds of thousands of nodes and millions of edges. QOCE follows the
popular seed set expansion strategy, regarding each high-quality maximal clique
as the initial seed set and applying quadratic optimization for the expansion.
We extensively evaluate our algorithm on 28 synthetic LFR networks and six
real-world networks of various domains and scales, and compare QOCE with four
state-of-the-art overlapping community detection algorithms. Empirical results
demonstrate the competitive performance of the proposed approach in terms of
detection accuracy, efficiency, and scalability.
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