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Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

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arxiv 2302.01018 v4 pith:GI3764LE submitted 2023-02-02 cs.LG cs.AI

Antonio Longa , Veronica Lachi , Gabriele Santin , Monica Bianchini , Bruno Lepri , Pietro Lio , Franco Scarselli , Andrea Passerini This is my paper
classification cs.LGcs.AI
keywords temporalgraphchallengesgnnsgraphslearningnetworksneural
verification ladder T0 review T1 audit T2 compute T3 formal
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Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current state-of-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction

    cs.AI 2025-07 conditional novelty 6.0 of 10

    EAGLE predicts temporal links with top-k recent neighbors plus top-k shared temporal PageRank influencers, matching or beating transformer T-GNNs while running far faster.

  2. DiT-SGCR: Directed Temporal Structural Representation with Global-Cluster Awareness for Ethereum Malicious Account Detection

    cs.CE 2025-06 reject novelty 5.0 of 10

    DiT-SGCR claims to improve Ethereum malicious account detection by 3.62% to 10.83% F1 over state-of-the-art via directed temporal clustering embeddings, but the supporting experiments and algorithm formulation have cr...

  3. Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn

    cs.LG 2025-06 conditional novelty 4.0 of 10

    At LinkedIn, a cross-domain GNN trained on a unified 8.6 billion-node graph with temporal modeling and multi-task learning reports a 0.62% CTR lift and a 0.10% WAU lift online.

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