ReaCH-TGN: Contrastive Hop and Time-Aware Temporal Graph Network for Reachability Prediction

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Zhengyi Yang

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Abstract

Temporal reachability prediction aims to determine whether a target node can be reached from a source node within a given time interval in a temporal graph. This problem is challenging due to the need to jointly capture evolving structural patterns, temporal dependencies, and multi-hop propagation. In this work, we propose an enhanced temporal graph embedding framework tailored for temporal reachability prediction. Previously, there was work on reachability prediction using random walk incorporating continuous time through constraints on transition probabilities and path frequency bias on starting node selection to solve this problem. Our method ReaCH-TGN (Reachability prediction with Contrastive Hop-Time aware Temporal Graph Network) integrates three key optimizations: (1) Hop-aware contrastive loss, which extends the NT-Xent objective by incorporating hop-dependent weights to emphasize more challenging long-range dependencies; (2) Time-gap penalty, which regularizes embeddings based on the temporal distance between events, improving recency sensitivity; and (3) Temporal data augmentation, which combines random event dropout with timestamp jitter to enhance robustness against temporal noise. Experiments on three real-world temporal networks show that our approach achieves significant improvements in accuracy across multiple hop distances, particularly in long-range reachability scenarios.

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@inproceedings{xu2025reachtgn,
  title = {ReaCH-TGN: Contrastive Hop and Time-Aware Temporal Graph Network for Reachability Prediction},
  author = {Xu, Zhuoqing and Cao, Xin and Yang, Zhengyi},
  editor = {Borovica-Gajic, Renata and Khan, Arijit and Zheng, Bolong and Wang, Xiaoyang and Gan, Junhao},
  booktitle = {Databases Theory and Applications},
  year = {2026},
  publisher = {Springer Nature Singapore},
  address = {Singapore},
  pages = {208--222},
  isbn = {978-981-95-6196-4}
}