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 boom 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.