融合局部全局特征的GNN提升有向图链接预测效果
Directed Link Prediction using GNN with Local and Global Feature Fusion
- 构建新GNN框架，融合节点嵌入与社区信息
- 在30%~60%训练数据下超越当前最优方法
- 适用于有向图链接预测任务的研究者
链接预测是图分析中的经典问题，具有广泛的实际应用。针对有向图，近期深度学习方法通常通过对比学习分析节点相似性，并利用图卷积聚合邻域信息。本文提出一种新型图神经网络（GNN）框架，融合特征嵌入与社区信息。理论上证明此类混合特征可提升有向链接预测性能。为高效利用这些特征，我们提出将输入图转换为有向线图的方法，使变换后的图中节点在图卷积过程中能聚合更多信息。在基准数据集上的实验表明，当使用30%、40%、50%和60%的连边作为训练数据时，本方法在多数情况下优于当前最优技术。
原文摘要 · Abstract (English)
Link prediction is a classical problem in graph analysis with many practical applications. For directed graphs, recently developed deep learning approaches typically analyze node similarities through contrastive learning and aggregate neighborhood information through graph convolutions. In this work, we propose a novel graph neural network (GNN) framework to fuse feature embedding with community information. We theoretically demonstrate that such hybrid features can improve the performance of directed link prediction. To utilize such features efficiently, we also propose an approach to transform input graphs into directed line graphs so that nodes in the transformed graph can aggregate more information during graph convolutions. Experiments on benchmark datasets show that our approach outperforms the state-of-the-art in most cases when 30%, 40%, 50%, and 60% of the connected links are used as training data, respectively.
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