Community detection in complex network based on APT method

Elsevier BV
Publication Type:
Journal Article
Pattern Recognition Letters, 2020, 138, pp. 193-200
Issue Date:
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© 2020 Elsevier B.V. Community detection is a significant methodology in network science. Traditional methods show limitations in dealing with multi-scale and high-dimensional complex data. As one of the most popular unsupervised algorithms, affinity propagation algorithm (AP) has been widely applied in community detection. However, its negative Euclidean similarity and inflexible parameter may lead to high time or memory consumption and excessive detection. Thus, this article presents a novel affinity propagation algorithm in t-distribution (APT), integrated with manifold learning, for detecting community structure. In APT algorithm, the data is compressed by dimensionality reduction, and joint probability is applied to construct the similarity matrix. Further, based on optimized modularity, parameters are adjusted to improve the accuracy. Experiments show that APT has better adaptability and universality than AP algorithm. In contrast to other mainstream algorithms, our algorithm can extract more meaningful communities from multi-scale and high-dimensional networks.
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