Directed Link Prediction using GNN with Local and Global Feature Fusion
This work addresses link prediction in directed graphs, which is important for applications like social network analysis, but it appears incremental as it builds on existing GNN and contrastive learning approaches.
The paper tackles directed link prediction by proposing a GNN framework that fuses feature embedding with community information, and it outperforms state-of-the-art methods on benchmark datasets with training data ranging from 30% to 60% of connected links.
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.