NCSAC: Effective Neural Community Search via Attribute-augmented Conductance

TL;DR

NCSAC introduces a novel neural community search method that combines attribute-augmented conductance with reinforcement learning to identify dense communities more accurately and efficiently.

Contribution

It proposes a new attribute-augmented conductance concept and integrates deep learning with rule-based constraints for improved community search.

Findings

Outperforms state-of-the-art methods in accuracy and efficiency.

Achieves up to 42.4% F1-score improvement.

Demonstrates scalability on large real-world graphs.

Abstract

Identifying locally dense communities closely connected to the user-initiated query node is crucial for a wide range of applications. Existing approaches either solely depend on rule-based constraints or exclusively utilize deep learning technologies to identify target communities. Therefore, an important question is proposed: can deep learning be integrated with rule-based constraints to elevate the quality of community search? In this paper, we affirmatively address this question by introducing a novel approach called Neural Community Search via Attribute-augmented Conductance, abbreviated as NCSAC. Specifically, NCSAC first proposes a novel concept of attribute-augmented conductance, which harmoniously blends the (internal and external) structural proximity and the attribute similarity. Then, NCSAC extracts a coarse candidate community of satisfactory quality using the proposed attribute-augmented conductance. Subsequently, NCSAC frames the community search as a graph optimization task, refining the candidate community through sophisticated reinforcement learning techniques, thereby producing high-quality results. Extensive experiments on six real-world graphs and ten competitors demonstrate the superiority of our solutions in terms of accuracy, efficiency, and scalability. Notably, the proposed solution outperforms state-of-the-art methods, achieving an impressive F1-score improvement ranging from 5.3\% to 42.4\%. For reproducibility purposes, the source code is available at https://github.com/longlonglin/ncsac.