mlsanityData to 14 Oct 2026

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs

arXiv 2510.07586 ↗First posted 8 Oct 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch 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

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 35 of its 47 references that are in the map. First posted on arXiv 8 Oct 2025.

Builds on

35 of 47 references.

References · 35

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23 more
  1. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  2. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
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  7. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 138
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  23. 2023Graph Mixer Networkscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioningcited by 0
  2. 2026Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphscited by 0
  3. 2026EdgeReMIND: A Scalable, Top-Ranked Memorization Baseline for Temporal Multi-Relational Link Predictioncited by 0