CommGNAS: Unsupervised Graph Neural Architecture Search for Community Detection

Abstract

Graph neural architecture search (GNAS) has been successful in many supervised learning tasks, such as node classification, graph classification, and link prediction. GNAS uses a search algorithm to sample graph neural network (GNN) architectures from the search space and evaluates sampled GNN architectures based on estimation strategies to generate feedback for the search algorithm. In traditional GNAS, the typical estimation strategy requires using labeled graph data to generate feedback, which plays a fundamental and vital role in the search algorithm to sample a better GNN architecture during the search process. However, a large portion of real-world graph data is unlabeled. The estimation strategy in traditional GNAS cannot use unlabeled graph data to generate feedback for the search algorithm, so the traditional supervised GNAS fails to solve unsupervised problems, such as community detection tasks. To solve this challenge, this paper proposed CommGNAS, an effective node representation learning method with unsupervised graph neural architecture search for community detection. In CommGNAS, we design an unsupervised evaluation strategy with self-supervised and self-representation learning. It represents the first research work in literature to solve the problems of unsupervised graph neural architecture search for community detection. The experimental results show that CommGNAS can obtain the best performance in community detection tasks on real-world graphs against the state-of-the-art baseline methods.