Social Group Query Based on Multi-fuzzy-constrained Strong Simulation

TL;DR

This paper introduces a novel strong simulation graph pattern matching algorithm (NTSS) for social group queries that incorporates rich contextual information and trust, improving accuracy and efficiency over existing methods.

Contribution

It proposes a new GPM algorithm considering node and edge credibility, with two optimization strategies, enhancing performance in social network analysis.

Findings

NTSS outperforms existing multi-constrained GPM algorithms.

Optimization strategies significantly improve matching efficiency.

Experimental results validate the effectiveness of the proposed methods.

Abstract

Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interaction between members of the group. Generally, graph pattern matching (GPM) is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this paper, a social group query problem that needs to consider the trust between members of the group is proposed. To solve this problem, we propose a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence. Aiming at the inefficiency of the NTSS algorithm when matching pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matching edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm.