Efficient Size Constraint Community Search over Heterogeneous Information Networks

2025-08-19

AI Summary

This paper addresses the resource-constrained community search problem in heterogeneous information networks (HINs), introducing a novel task: *size-constrained cohesive community discovery*—finding the most structurally cohesive subgraph containing a given query node and satisfying a user-specified size constraint. To capture cohesion in HINs, we generalize the *(k, P)-truss* model to incorporate cross-type edge constraints. We propose an exact branch-and-bound (B&B) algorithm integrating structural-aware pruning, joint node-edge enumeration, heuristic initialization, and global lower-bound optimization. Additionally, we design an efficient heuristic algorithm to enhance scalability. Extensive experiments on multiple real-world HIN datasets demonstrate that our methods significantly outperform state-of-the-art baselines in both solution quality and efficiency.

Technology Category

• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Social mining and social search on the Web
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

The goal of community search in heterogeneous information networks (HINs) is to identify a set of closely related target nodes that includes a query target node. In practice, a size constraint is often imposed due to limited resources, which has been overlooked by most existing HIN community search works. In this paper, we introduce the size-bounded community search problem to HIN data. Specifically, we propose a refined (k, P)-truss model to measure community cohesiveness, aiming to identify the most cohesive community of size s that contains the query node. We prove that this problem is NP-hard. To solve this problem, we develop a novel B&B framework that efficiently generates target node sets of size s. We then tailor novel bounding, branching, total ordering, and candidate reduction optimisations, which enable the framework to efficiently lead to an optimum result. We also design a heuristic algorithm leveraging structural properties of HINs to efficiently obtain a high-quality initial solution, which serves as a global lower bound to further enhance the above optimisations. Building upon these, we propose two exact algorithms that enumerate combinations of edges and nodes, respectively. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed methods.

Problem

• Identifies size-constrained communities in heterogeneous networks
• Solves NP-hard problem of finding most cohesive s-sized community
• Develops exact algorithms with optimizations for efficient search

Innovation

• Refined (k, P)-truss model for community cohesiveness
• Novel B&B framework with optimizations for efficiency
• Exact algorithms enumerating edge and node combinations