计算机科学
雅卡索引
进化算法
数据挖掘
节点(物理)
加权
图形
特征(语言学)
算法
聚类分析
人工智能
理论计算机科学
医学
结构工程
工程类
放射科
语言学
哲学
作者
Chao Gao,Ze Yin,Zhen Wang,Xianghua Li,Xuelong Li
出处
期刊:IEEE Computational Intelligence Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-05-01
卷期号:18 (2): 46-59
被引量:9
标识
DOI:10.1109/mci.2023.3245729
摘要
In recent years, multilayer networks have served as effective models for addressing and analyzing real-world systems with multiple relationships. Among these scenarios, the community detection (CD) problem is one of the most prominent research hotspots. Although some research on multilayer network CD (MCD) has been proposed to address this problem, most studies focus only on topological structures. Therefore, their algorithms cannot extract the most out of complementary network information, such as node similarities and low-rank features, which may lead to unsatisfactory accuracy. To tackle this problem, this paper proposes a novel multi-objective evolutionary algorithm based on consensus prior information (MOEA-CPI). The proposed algorithm takes full advantage of prior information to guide the MOEA with respect to topological structures, initializations, and the optimization process. More specifically, this paper first extracts two kinds of prior information, i.e., graph-level and node-level information, based on Node2vec and Jaccard similarity, respectively. Then, the prior layer and a high-quality initial population are constructed on the basis of the graph-level information. During the optimization process, the genetic operator, which integrates the weighting strategy and node-level information, is applied to guide the algorithm to distribute similar nodes into the same community. Extensive experiments are implemented to prove the superior performance of MOEA-CPI over the state-of-the-art methods.
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