PLACE: Prompt Learning for Attributed Community Search in Large Graphs

Attributed Community Search (ACS) aims to identify communities in an attributed graph with structural cohesiveness and attribute homogeneity for given queries. While algorithmic approaches often suffer from structural inflexibility and attribute irrelevance, recent years have witnessed a broom in learning-based approaches that employ Graph Neural Networks (GNNs) to simultaneously model structural and attribute information. To improve GNN efficacy and efficiency, these approaches adopt various techniques to refine the input graph and queries, including query-dependent node pruning and feature enhancement. However, these refinements are either detached from the end-to-end optimization with the backbone model, or impose additional burdens on training resources, limiting their adaptability and practical usage in diverse graphs.

In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural Language Processing (NLP), where learnable prompt tokens are inserted to contextualize NLP queries, PLACE integrates structural and learnable prompt tokens into the graph as a query-dependent refinement mechanism, forming a prompt-augmented graph. Within this prompt-augmented graph structure, the learned prompt tokens serve as a bridge that strengthens connections between graph nodes for the query, enabling the GNN to more effectively identify patterns of structural cohesiveness and attribute similarity related to the specific query. We employ an alternating training paradigm to optimize both the prompt parameters and the GNN jointly. Moreover, we design a divide-and-conquer strategy to enhance scalability, supporting the model to handle million-scale graphs. Extensive experiments on 9 real-world graphs demonstrate the effectiveness of PLACE for three types of ACS queries, where PLACE achieves higher F1 scores by 14% compared to the state-of-the-arts on average.