DAG: Deep Adaptive and Generative $K$-Free Community Detection on Attributed Graphs

TL;DR

This paper introduces DAG, a novel deep learning model for community detection in attributed graphs that does not require prior knowledge of the number of communities, enabling end-to-end detection and community number search.

Contribution

DAG is the first model to perform community detection without pre-specifying the number of communities, integrating community number search into the learning process.

Findings

DAG outperforms state-of-the-art methods on five public datasets.

DAG achieves a 7.35% increase in team detection in a Tencent online game.

The EDGE metric effectively evaluates community detection without labels.

Abstract

Community detection on attributed graphs with rich semantic and topological information offers great potential for real-world network analysis, especially user matching in online games. Graph Neural Networks (GNNs) have recently enabled Deep Graph Clustering (DGC) methods to learn cluster assignments from semantic and topological information. However, their success depends on the prior knowledge related to the number of communities $K$, which is unrealistic due to the high costs and privacy issues of acquisition.In this paper, we investigate the community detection problem without prior $K$, referred to as $K$-Free Community Detection problem. To address this problem, we propose a novel Deep Adaptive and Generative model~(DAG) for community detection without specifying the prior $K$. DAG consists of three key components, \textit{i.e.,} a node representation learning module with masked attribute reconstruction, a community affiliation readout module, and a community number search module with group sparsity. These components enable DAG to convert the process of non-differentiable grid search for the community number, \textit{i.e.,} a discrete hyperparameter in existing DGC methods, into a differentiable learning process. In such a way, DAG can simultaneously perform community detection and community number search end-to-end. To alleviate the cost of acquiring community labels in real-world applications, we design a new metric, EDGE, to evaluate community detection methods even when the labels are not feasible. Extensive offline experiments on five public datasets and a real-world online mobile game dataset demonstrate the superiority of our DAG over the existing state-of-the-art (SOTA) methods. DAG has a relative increase of 7.35\% in teams in a Tencent online game compared with the best competitor.