Structure Enhanced Graph Neural Networks for Link Prediction

TL;DR

This paper introduces Structure Enhanced Graph Neural Networks (SEG) that incorporate topological information via path labeling to improve link prediction accuracy, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper proposes a novel structure encoder that, combined with GNNs, effectively captures topological information for link prediction.

Findings

SEG outperforms existing models on OGB datasets

Incorporating topological structure improves link prediction accuracy

Joint training of structure encoder and GNN enhances performance

Abstract

Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one. GNN models usually follow a node-centric message passing procedure that aggregates the neighborhood information to the central node recursively. Following this paradigm, features of nodes are passed through edges without caring about where the nodes are located and which role they played. However, the neglected topological information is shown to be valuable for link prediction tasks. In this paper, we propose Structure Enhanced Graph neural network (SEG) for link prediction. SEG introduces the path labeling method to capture surrounding topological information of target nodes and then incorporates the structure into an ordinary GNN model. By jointly training the structure encoder and deep GNN model, SEG fuses topological structures and node features to take full advantage of graph information. Experiments on the OGB link prediction datasets demonstrate that SEG achieves state-of-the-art results among all three public datasets.