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 22% compared to the state-of-the-arts on average.

翻译：本文提出PLACE（Prompt Learning for Attributed Community Search），一种面向属性社区搜索（ACS）的创新图提示学习框架。受自然语言处理（NLP）中提示调优（prompt-tuning）的启发（该方法通过插入可学习的提示标记对NLP查询进行上下文化），PLACE将结构性和可学习的提示标记作为查询相关的细化机制整合到图中，形成提示增强图。在此提示增强图结构中，学习到的提示标记充当桥梁，加强了查询相关图节点间的连接，使图神经网络（GNN）能够更有效地识别与特定查询相关的结构凝聚性和属性相似性模式。我们采用交替训练范式联合优化提示参数和GNN。此外，我们设计了一种分治策略以增强可扩展性，支持模型处理百万级规模图。在9个真实世界图上的大量实验表明，PLACE在处理三种类型ACS查询时的有效性，其F1分数平均比现有最先进方法提高22%。