Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks

TL;DR

This paper investigates how different time granularities affect the performance of Dynamic Graph Neural Networks in dynamic link prediction tasks across various domains, emphasizing the importance of memory mechanisms and temporal resolution.

Contribution

It provides an extensive experimental analysis of the impact of time granularity on DGNN performance, highlighting the significance of memory mechanisms and offering insights for future research.

Findings

Proper time granularity improves model robustness.

Memory mechanisms are crucial for dynamic link prediction.

Optimal time granularity varies across models and datasets.

Abstract

Dynamic Graph Neural Networks (DGNNs) have emerged as the predominant approach for processing dynamic graph-structured data. However, the influence of temporal information on model performance and robustness remains insufficiently explored, particularly regarding how models address prediction tasks with different time granularities. In this paper, we explore the impact of time granularity when training DGNNs on dynamic graphs through extensive experiments. We examine graphs derived from various domains and compare three different DGNNs to the baseline model across four varied time granularities. We mainly consider the interplay between time granularities, model architectures, and negative sampling strategies to obtain general conclusions. Our results reveal that a sophisticated memory mechanism and proper time granularity are crucial for a DGNN to deliver competitive and robust performance in the dynamic link prediction task. We also discuss drawbacks in considered models and datasets and propose promising directions for future research on the time granularity of temporal graphs.