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

被引：0
|
作者：
Liu, Guliu [1 ,2 ]
Li, Lei [1 ,2 ]
Liu, Guanfeng [3 ]
Wu, Xindong [1 ,2 ]
机构：
[1] Hefei Univ Technol, Minist Educ, Key Lab Knowledge Engn Big Data, Hefei 230601, Peoples R China
[2] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230601, Peoples R China
[3] Macquarie Univ, Dept Comp, Sydney, NSW 2122, Australia
来源：
INFORMATION SCIENCES | 2025年 / 690卷
基金：
中国国家自然科学基金;
关键词：
Multi-constrained graph pattern matching; Group discovery; Multi-constrained optimal path selection; Fixed number of nodes pattern matching; ALGORITHM; SELECTION;
D O I：
10.1016/j.ins.2024.121571
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页数：19
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