Generative Graph Neural Networks for Link Prediction

TL;DR

This paper introduces GraphLP, a generative deep learning approach for link prediction in graphs that leverages network reconstruction theory and high-order connectivity patterns, outperforming existing discriminative methods.

Contribution

Proposes a novel generative link prediction algorithm, GraphLP, that automatically learns structural graph features and utilizes hierarchical connectivity, differing from traditional discriminative models.

Findings

GraphLP outperforms state-of-the-art methods on benchmark datasets.

It leverages high-order connectivity patterns for improved accuracy.

The method demonstrates robustness across various graph applications.

Abstract

Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link prediction and have achieved state-of-the-art performance. Nevertheless, existing methods developed for this purpose are typically discriminative, computing features of local subgraphs around two neighboring nodes and predicting potential links between them from the perspective of subgraph classification. In this formalism, the selection of enclosing subgraphs and heuristic structural features for subgraph classification significantly affects the performance of the methods. To overcome this limitation, this paper proposes a novel and radically different link prediction algorithm based on the network reconstruction theory, called GraphLP. Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated. Moreover, GraphLP explores high-order connectivity patterns to utilize the hierarchical organizational structures of graphs for link prediction. Our experimental results on all common benchmark datasets from different applications demonstrate that the proposed method consistently outperforms other state-of-the-art methods. Unlike the discriminative neural network models used for link prediction, GraphLP is generative, which provides a new paradigm for neural-network-based link prediction.