Temporal receptive field in dynamic graph learning: A comprehensive analysis

TL;DR

This paper provides a comprehensive analysis of the temporal receptive field in dynamic graph learning, demonstrating its crucial impact on model accuracy and offering guidelines for optimal window selection.

Contribution

It formalizes the role of temporal receptive field in dynamic graph models and evaluates its influence across multiple datasets and models.

Findings

Properly chosen temporal receptive fields improve prediction accuracy.

Overly large windows can introduce noise and reduce performance.

Extensive benchmarking validates the analysis and findings.

Abstract

Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of the temporal receptive field, which refers to the temporal context that models use for making predictions, has been largely overlooked and insufficiently analyzed in existing research. In this study, we present a comprehensive analysis of the temporal receptive field in dynamic graph learning. By examining multiple datasets and models, we formalize the role of temporal receptive field and highlight their crucial influence on predictive accuracy. Our results demonstrate that appropriately chosen temporal receptive field can significantly enhance model performance, while for some models, overly large windows may introduce noise and reduce accuracy. We conduct extensive benchmarking to validate our findings, ensuring that all experiments are fully reproducible. Code is available at https://github.com/ykrmm/BenchmarkTW .