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（面向属性社区搜索的提示学习框架），这是一种创新的图提示学习框架，用于属性社区搜索。受自然语言处理中提示调优方法的启发——该方法通过插入可学习的提示词符来情境化自然语言查询，PLACE将结构化和可学习的提示词符集成到图中，形成一种依赖于查询的优化机制，从而构建出提示增强图。在此提示增强图结构中，学习到的提示词符作为桥梁，强化了图中节点与查询之间的关联，使图神经网络能够更有效地识别与特定查询相关的结构内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图神经网络。此外，我们设计了分治策略以提升可扩展性，使模型能够处理百万级规模的图结构。在9个真实世界图数据上的大量实验表明，PLACE对三类属性社区查询均具有显著有效性，其F1分数平均比现有最优方法提升22%。