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Continuous Encoding for Overlapping Community Detection in Attributed Network
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AI
DOI：10.1109/TCYB.2022.3155646.png)
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Abstract
En 中文
Detecting overlapping communities of an attribute network is a ubiquitous yet very difficult task, which can be modeled as a discrete optimization problem. Besides the topological structure of the network, node attributes and node overlapping aggravate the difficulty of community detection significantly. In this article, we propose a novel continuous encoding method to convert the discrete-natured detection problem to a continuous one by associating each edge and node attribute in the network with a continuous variable. Based on the encoding, we propose to solve the converted continuous problem by a multiobjective evolutionary algorithm (MOEA) based on decomposition. To find the overlapping nodes, a heuristic based on double-decoding is proposed, which is only with linear complexity. Furthermore, a postprocess community merging method in consideration of node attributes is developed to enhance the homogeneity of nodes in the detected communities. Various synthetic and real-world networks are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed approach performs significantly better than a variety of evolutionary and nonevolutionary methods on most of the benchmark networks.
Keywords:
Encoding
Peer-to-peer computing
Optimization
Measurement
Image edge detection
Decoding
Complex networks
Attribute network
continuous encoding method
multiobjective evolutionary algorithm (MOEA)
overlapping communities
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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