用可学习提示增强图结构，提升大规模图中社区搜索精度
PLACE: Prompt Learning for Attributed Community Search in Large Graphs
- 将可学习提示嵌入图中，动态优化查询相关节点连接
- 在9个真实图上平均比现有方法高22%的F1分数
- 适合处理百万级节点的大规模图社区搜索任务
本文提出PLACE（Prompt Learning for Attributed Community Search），一种面向属性社区搜索（ACS）的图提示学习框架。受自然语言处理中提示调优启发，PLACE将结构信息与可学习提示令牌结合，形成查询相关的提示增强图结构。在此结构中，学习到的提示令牌作为桥梁，强化查询相关节点间的连接，使图神经网络更有效识别结构凝聚性与属性相似性。采用交替训练策略联合优化提示参数与GNN。设计分治策略提升可扩展性，支持百万级图。在9个真实数据集上的实验表明，对于三类ACS查询，PLACE平均较最先进方法提升22%的F1分数。
原文摘要 · Abstract (English)
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.
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