TGM: A Modular Framework for Machine Learning on Temporal Graphs

Jacob Chmura, Shenyang Huang, Ali Parviz, Farimah Poursafaei, Michael M. Bronstein, Guillaume Rabusseau, Matthias Fey, Reihaneh Rabbany

Keywords: Temporal Graph Learning, Dynamic Graphs, Deep Learning, Programming Framework, Software Libraries

TL;DR: We present TGM, a modular research library for efficient and reproducible machine learning on temporal graphs. TGM also supports both discrete and continuous-time dynamic graph methods.

Abstract: While deep learning on static graphs has been revolutionized by standardized libraries like PyTorch Geometric and DGL, machine learning on Temporal Graphs (TG), networks that evolve over time, lacks comparable software infrastructure. Existing TG libraries are limited in scope, focusing on a single method category or specific algorithms. We introduce Temporal Graph Modelling (TGM), a comprehensive framework for machine learning on temporal graphs to address this gap. Through a modular architecture, TGM is the first library to support both discrete and continuous-time TG methods and implements a wide range of TG methods. The TGM framework combines an intuitive front-end API with an optimized backend storage, enabling reproducible research and efficient experimentation at scale. Key features include graph-level optimizations for offline training and built-in performance profiling capabilities. Through extensive benchmarking on five real-world networks, TGM is up to 6 times faster than the widely used DyGLib library on TGN and TGAT models and up to 8 times faster than the UTG framework for converting edges into coarse-grained snapshots.

Submission Number: 20