Size-fixed group discovery via multi-constrained graph pattern matching

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-22 DOI:10.1016/j.ins.2024.121571

Guliu Liu , Lei Li , Guanfeng Liu , Xindong Wu

{"title":"Size-fixed group discovery via multi-constrained graph pattern matching","authors":"Guliu Liu , Lei Li , Guanfeng Liu , Xindong Wu","doi":"10.1016/j.ins.2024.121571","DOIUrl":null,"url":null,"abstract":"<div><div>Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121571"},"PeriodicalIF":8.1000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020025524014853","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

通过多约束图模式匹配发现大小固定的群组

多约束图模式匹配（Multi-Constrained Graph Pattern Matching，MC-GPM）旨在匹配节点和边上有多个属性约束的模式图，在基于社交的电子商务和基于信任的群体发现等多个领域引起了广泛关注。然而，现有的 MC-GPM 方法没有考虑到模式图中每个节点的数量需要固定的情况，例如寻找专家数量和关系指定的专家组。本文提出了节点数固定的多约束强模拟（MCSS-FNN）匹配模型，并设计了一种面向信任的多约束最优路径（TOMP）匹配算法来解决该问题。此外，还设计了两种启发式优化策略，一种用于组合测试，另一种用于边缘匹配，以提高 TOMP 算法的效率。我们在四个真实的社交网络数据集上进行了实证实验，结果证明了所提算法和优化策略的有效性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.

期刊最新文献

Editorial Board Community structure testing by counting frequent common neighbor sets Finite-time secure synchronization for stochastic complex networks with delayed coupling under deception attacks: A two-step switching control scheme Adaptive granular data compression and interval granulation for efficient classification Introducing fairness in network visualization