LFP: Layer Wise Feature Perturbation based Graph Neural Network for Link Prediction

Learning on graph-structured data is an area where graph neural networks (GNN) have gained widespread use. In several tasks, such as node classification and graph classification, they outperformed traditional heuristic techniques. When it comes to link prediction, where the edge features, particularly multi-dimensional edge data, are critical, GNNs generally perform poorly compared to simple heuristic approaches. In this research, we provide a novel method for graph neural networks family which can better explore edge characteristics. These features may include both directed and undirected edges, as well as edges with many dimensions. The suggested framework has the potential to unify existing models of graph neural networks like GCN and GAT. We build a new method for edge perturbation for every GNN layer which can process edge features with more than one dimension. We test our proposed model for graph link prediction on a wide range of publicly available graph datasets. Our proposed method surpass the existing state-of-the-art approaches, employed based on GCNs and GAT, demonstrating the significance of leveraging edge properties for graph neural networks.