mlsanityData to 14 Oct 2026

A Primer on Temporal Graph Learning

arXiv 2401.03988 ↗First posted 8 Jan 2024cs.LG cs.AI cs.DMcited by 5

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Abstract (arXiv)

This document aims to familiarize readers with temporal graph learning (TGL) through a concept-first approach. We have systematically presented vital concepts essential for understanding the workings of a TGL framework. In addition to qualitative explanations, we have incorporated mathematical formulations where applicable, enhancing the clarity of the text. Since TGL involves temporal and spatial learning, we introduce relevant learning architectures ranging from recurrent and convolutional neural networks to transformers and graph neural networks. We also discuss classical time series forecasting methods to inspire interpretable learning solutions for TGL.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 50 of its 112 references that are in the map. First posted on arXiv 8 Jan 2024.

Builds on

50 of 112 references.

References · 50

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38 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 4.

5 papers.

  1. 2026Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspectivecited by 4
  2. 2026Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphscited by 1
  3. 2024Online Algorithm for Node Feature Forecasting in Temporal Graphscited by 0
  4. 2026Generating Causal Temporal Interaction Graphs for Counterfactual Validation of Temporal Link Predictioncited by 0
  5. 2026A temporal graph dissimilarity measure capturing epidemic spreading behaviorcited by 0