通过社区结构增强图表示，提升链接预测准确率。
A Community-Enhanced Graph Representation Model for Link Prediction
- 利用社区感知的边补全与修剪，融合多尺度结构特征。
- 在多个基准数据集上优于传统方法和现有GNN模型。
- 适合需要精准链接预测的研究者，尤其关注图结构建模。
尽管图神经网络（GNNs）已成为图表示学习的主流方法，但在链接预测任务上的表现并未始终超越传统启发式方法（如共同邻居、Jaccard系数）。这主要是因为现有GNN倾向于学习局部节点表示，难以有效捕捉节点对之间的结构关系，且过度依赖局部邻域信息易导致过平滑。已有研究指出引入全局结构编码可部分缓解此问题。为此，本文提出社区增强的链接预测框架（CELP），通过社区结构联合建模局部与全局图拓扑。具体而言，CELP采用社区感知、置信度引导的边补全与剪枝策略，并整合多尺度结构特征，实现更精准的链接预测。在多个基准数据集上的实验结果表明，CELP性能显著优于基线方法，验证了社区结构在提升链接预测准确性中的关键作用。
原文摘要 · Abstract (English)
Although Graph Neural Networks (GNNs) have become the dominant approach for graph representation learning, their performance on link prediction tasks does not always surpass that of traditional heuristic methods such as Common Neighbors and Jaccard Coefficient. This is mainly because existing GNNs tend to focus on learning local node representations, making it difficult to effectively capture structural relationships between node pairs. Furthermore, excessive reliance on local neighborhood information can lead to over-smoothing. Prior studies have shown that introducing global structural encoding can partially alleviate this issue. To address these limitations, we propose a Community-Enhanced Link Prediction (CELP) framework that incorporates community structure to jointly model local and global graph topology. Specifically, CELP enhances the graph via community-aware, confidence-guided edge completion and pruning, while integrating multi-scale structural features to achieve more accurate link prediction. Experimental results across multiple benchmark datasets demonstrate that CELP achieves superior performance, validating the crucial role of community structure in improving link prediction accuracy.
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