Portrait de Jacob Chmura

Jacob Chmura

Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
Nicolas Le Roux
Co-supervisor
Reihaneh Rabbany
Sujets de recherche
Apprentissage profond
Exploration des données
Optimisation
Réseaux de neurones en graphes
Site web
GitHub

Publications

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura
Shenyang Huang
Tran Gia Bao Ngo
Ali Parviz
Farimah Poursafaei
Jure Leskovec
Michael M. Bronstein
Guillaume Rabusseau
Matthias Fey
Reihaneh Rabbany
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like Py… (voir plus)Torch Geometric and DGL, ML for temporal graphs (TG), networks that evolve over time, lacks comparable infrastructure. Existing TG libraries are often tailored to specific architectures, hindering support for diverse models in this rapidly evolving field. Additionally, the divide between continuous- and discrete-time dynamic graph methods (CTDG and DTDG) limits direct comparisons and idea transfer. To address these gaps, we introduce Temporal Graph Modelling (TGM), a research-oriented library for ML on temporal graphs, the first to unify CTDG and DTDG approaches. TGM offers first-class support for dynamic node features, time-granularity conversions, and native handling of link-, node-, and graph-level tasks. Empirically, TGM achieves an average 7.8x speedup across multiple models, datasets, and tasks compared to the widely used DyGLib, and an average 175x speedup on graph discretization relative to available implementations. Beyond efficiency, we show in our experiments how TGM unlocks entirely new research possibilities by enabling dynamic graph property prediction and time-driven training paradigms, opening the door to questions previously impractical to study. TGM is available at https://github.com/tgm-team/tgm
2025-10-08
ArXiv (prépublication)
arxiv.org
arxiv.org
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura
Shenyang Huang
Tran Gia Bao Ngo
Ali Parviz
Farimah Poursafaei
Jure Leskovec
Michael M. Bronstein
Guillaume Rabusseau
Matthias Fey
Reihaneh Rabbany
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like Py… (voir plus)Torch Geometric and DGL, ML for temporal graphs (TG), networks that evolve over time, lacks comparable infrastructure. Existing TG libraries are often tailored to specific architectures, hindering support for diverse models in this rapidly evolving field. Additionally, the divide between continuous- and discrete-time dynamic graph methods (CTDG and DTDG) limits direct comparisons and idea transfer. To address these gaps, we introduce Temporal Graph Modelling (TGM), a research-oriented library for ML on temporal graphs, the first to unify CTDG and DTDG approaches. TGM offers first-class support for dynamic node features, time-granularity conversions, and native handling of link-, node-, and graph-level tasks. Empirically, TGM achieves an average 7.8x speedup across multiple models, datasets, and tasks compared to the widely used DyGLib, and an average 175x speedup on graph discretization relative to available implementations. Beyond efficiency, we show in our experiments how TGM unlocks entirely new research possibilities by enabling dynamic graph property prediction and time-driven training paradigms, opening the door to questions previously impractical to study. TGM is available at https://github.com/tgm-team/tgm
2025-10-08
ArXiv (prépublication)
arxiv.org
arxiv.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Micheal Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Michael Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Micheal Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
AIF-GEN: Open-Source Platform and Synthetic Dataset Suite for Reinforcement Learning on Large Language Models
Jacob Chmura
Shahrad Mohammadzadeh
Ivan Anokhin
Jacob-Junqi Tian
Mandana Samiei
Taz Scott-Talib
Irina Rish
Doina Precup
Reihaneh Rabbany
Nishanth Anand
2025-06-09
ICML.cc/2025/Workshop/CODEML (publié)
openreview.net
openreview.net
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
While deep learning on static graphs has been revolutionized by standardized libraries like PyTorch Geometric and DGL, machine learning on T… (voir plus)emporal 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.
2025-06-09
ICML.cc/2025/Workshop/CODEML (publié)
openreview.net
openreview.net