Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs

TL;DR

This paper introduces GSNOP, a neural process-based method that models link prediction on dynamic, sparse graphs as a stochastic process, incorporating uncertainty and improving performance over existing methods.

Contribution

The paper proposes GSNOP, a novel neural process approach that combines neural ODEs for dynamic link prediction, effectively handling data sparsity and uncertainty.

Findings

GSNOP outperforms existing DGNNs on three datasets.

Incorporates uncertainty into link prediction.

Compatible with various DGNN structures.

Abstract

Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of historical data (interactions over time), which is not always available in practice. The missing links over time, which is a common phenomenon in graph data, further aggravates the issue and thus creates extremely sparse and dynamic graphs. To address this problem, we propose a novel method based on the neural process, called Graph Sequential Neural ODE Process (GSNOP). Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process. By defining a distribution over functions, GSNOP introduces the uncertainty into the predictions, making it generalize to more situations instead of overfitting to the sparse data. GSNOP is also agnostic to model structures that can be integrated with any DGNN to consider the chronological and geometrical information for link prediction. Extensive experiments on three dynamic graph datasets show that GSNOP can significantly improve the performance of existing DGNNs and outperform other neural process variants.