CLARE: A Semi-supervised Community Detection Algorithm

TL;DR

CLARE is a semi-supervised community detection algorithm that efficiently locates and refines communities in networks using deep reinforcement learning, improving accuracy and flexibility over seed-based methods.

Contribution

The paper introduces CLARE, a novel semi-supervised community detection method combining community locator and rewriter components with reinforcement learning for better community identification.

Findings

Outperforms state-of-the-art methods on real-world datasets.

Achieves higher accuracy in community detection tasks.

Reduces computational overhead compared to existing approaches.

Abstract

Community detection refers to the task of discovering closely related subgraphs to understand the networks. However, traditional community detection algorithms fail to pinpoint a particular kind of community. This limits its applicability in real-world networks, e.g., distinguishing fraud groups from normal ones in transaction networks. Recently, semi-supervised community detection emerges as a solution. It aims to seek other similar communities in the network with few labeled communities as training data. Existing works can be regarded as seed-based: locate seed nodes and then develop communities around seeds. However, these methods are quite sensitive to the quality of selected seeds since communities generated around a mis-detected seed may be irrelevant. Besides, they have individual issues, e.g., inflexibility and high computational overhead. To address these issues, we propose CLARE, which consists of two key components, Community Locator and Community Rewriter. Our idea is that we can locate potential communities and then refine them. Therefore, the community locator is proposed for quickly locating potential communities by seeking subgraphs that are similar to training ones in the network. To further adjust these located communities, we devise the community rewriter. Enhanced by deep reinforcement learning, it suggests intelligent decisions, such as adding or dropping nodes, to refine community structures flexibly. Extensive experiments verify both the effectiveness and efficiency of our work compared with prior state-of-the-art approaches on multiple real-world datasets.