用预训练+提示词提升社区发现效率，更快更准。
Pre-trained Prompt-driven Semi-supervised Local Community Detection
- 用图神经网络学节点表示，再选结构相似的已知社区作样本。
- 在五个数据集上，社区质量与效率均优于现有方法。
- 适合需要快速定位局部社区的场景，如社交网络分析。
半监督局部社区发现旨在利用已知社区来识别包含给定节点的社区。尽管现有研究取得良好效果，但仍存在耗时问题，亟需更高效的算法。为此，本文引入“预训练、提示”范式，提出预训练提示驱动的半监督局部社区发现方法（PPSL）。PPSL包含三个核心模块：节点编码、样本生成和提示驱动微调。节点编码模块使用图神经网络学习节点与社区的表示；基于这些表示，样本生成模块选取与目标节点局部结构相似的已知社区作为训练样本；最后，提示驱动微调模块利用这些样本作为提示，指导最终社区预测。在五个真实数据集上的实验表明，PPSL在社区质量与效率方面均优于基线方法。
原文摘要 · Abstract (English)
Semi-supervised local community detection aims to leverage known communities to detect the community containing a given node. Although existing semi-supervised local community detection studies yield promising results, they suffer from time-consuming issues, highlighting the need for more efficient algorithms. Therefore, we apply the "pre-train, prompt" paradigm to semi-supervised local community detection and propose the Pre-trained Prompt-driven Semi-supervised Local community detection method (PPSL). PPSL consists of three main components: node encoding, sample generation, and prompt-driven fine-tuning. Specifically, the node encoding component employs graph neural networks to learn the representations of nodes and communities. Based on representations of nodes and communities, the sample generation component selects known communities that are structurally similar to the local structure of the given node as training samples. Finally, the prompt-driven fine-tuning component leverages these training samples as prompts to guide the final community prediction. Experimental results on five real-world datasets demonstrate that PPSL outperforms baselines in both community quality and efficiency.
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