Efficient Size Constraint Community Search over Heterogeneous Information Networks

TL;DR

This paper addresses size-constrained community search in heterogeneous information networks by introducing a new problem formulation, proving its NP-hardness, and developing efficient exact and heuristic algorithms validated through extensive experiments.

Contribution

It introduces the size-bounded community search problem in HINs, proposes a refined (k, P)-truss model, and develops novel algorithms with optimizations for efficient solutions.

Findings

The proposed algorithms effectively find optimal communities under size constraints.

Heuristic methods provide high-quality initial solutions, improving overall efficiency.

Extensive experiments demonstrate the methods' effectiveness and scalability on real datasets.

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.