Multi-Community Search Using Similarity Enhanced Random Walk in Attributed Networks

被引：0
|
作者：
Li Q.-Q. [1 ]
Ma H.-F. [1 ,2 ]
Li J. [1 ]
Li Z.-X. [3 ]
机构：
[1] College of Computer Science and Engineering, Northwest Normal University, Lanzhou
[2] Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Science and Technology, Guilin
[3] College of Computer Science and Information Engineering, Guangxi Normal University, Guilin
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2021年 / 49卷 / 11期
关键词：
Attributed networks; Community search; High-order structure; Parallel conductance; Similarity-enhanced random walk;
D O I：
10.12263/DZXB.20200422
中图分类号：
学科分类号：
摘要：
Community search aims to find personalized communities highly related to the given query nodes. Existing community search methods are applicable to simple networks, and always assume either a single query node is given or multiple query nodes are from the same community, which limits the flexibility of the algorithm. An attributed network oriented multi-community search method, which is designed via random walk path similarity enhancement of query nodes, is proposed to effectively locate multiple local communities that query node belongs. Attribute and high-order structure information in the network are effectively fused, and the importance score vector of each query node is calculated based on random walk with restart. The similarity between random walk paths of query nodes is calculated and the similarity enhancement strategy is designed to enhance the association of similar path walkers so as to locate multiple community structures of different query nodes. Based on the combination of structure and attribute, the parallel conductance is used to accurately find the community. The experiments on both real-world datasets and synthetic datasets verify the effectiveness and efficiency of the proposed method. © 2021, Chinese Institute of Electronics. All right reserved.
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页码：2096 / 2100
页数：4
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