mlsanityData to 14 Oct 2026

A Survey on Temporal Graph Representation Learning and Generative Modeling

arXiv 2208.12126 ↗First posted 25 Aug 2022cs.LGcited by 4

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Abstract (arXiv)

Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e commerce, communication, road networks, biological systems, and many more. They necessitate research beyond the work related to static graphs in terms of their generative modeling and representation learning. In this survey, we comprehensively review the neural time dependent graph representation learning and generative modeling approaches proposed in recent times for handling temporal graphs. Finally, we identify the weaknesses of existing approaches and discuss the research proposal of our recently published paper TIGGER[24].

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 48 of its 88 references that are in the map. First posted on arXiv 25 Aug 2022.

Builds on

48 of 88 references.

References · 48

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36 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  2. 2025A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunitiescited by 4
  3. 2024Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negativescited by 1
  4. 2024Acceleration Algorithms in GNNs: A Surveycited by 0