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Temporal receptive field in dynamic graph learning: A comprehensive analysis

arXiv 2407.12370 ↗First posted 17 Jul 2024cs.LGcited by 0

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Abstract (arXiv)

Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of the temporal receptive field, which refers to the temporal context that models use for making predictions, has been largely overlooked and insufficiently analyzed in existing research. In this study, we present a comprehensive analysis of the temporal receptive field in dynamic graph learning. By examining multiple datasets and models, we formalize the role of temporal receptive field and highlight their crucial influence on predictive accuracy. Our results demonstrate that appropriately chosen temporal receptive field can significantly enhance model performance, while for some models, overly large windows may introduce noise and reduce accuracy. We conduct extensive benchmarking to validate our findings, ensuring that all experiments are fully reproducible. Code is available at https://github.com/ykrmm/BenchmarkTW .

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 17 Jul 2024.

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References · 24

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12 more
  1. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 193
  2. 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 132
  3. 2020Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A surveycited by 124
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  5. 2022Transformer for Graphs: An Overview from Architecture Perspectivecited by 107
  6. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  7. 2021Anomaly Detection in Dynamic Graphs via Transformercited by 59
  8. 2018GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Predictioncited by 47
  9. 2020Do RNN and LSTM have Long Memory?cited by 47
  10. 2021A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Predictioncited by 28
  11. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  12. 2024Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenizationcited by 2

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