Long Range Propagation on Continuous-Time Dynamic Graphs
<|reference_start|>Long Range Propagation on Continuous-Time Dynamic Graphs: Learning Continuous-Time Dynamic Graphs (C-TDGs) requires accurately modeling spatio-temporal information on streams of irregularly sampled events. While many methods have been proposed recently, we find that most message passing-, recurrent- or self-attention-based methods perform poorly on long-range tasks. These tasks require correlating information that occurred "far" away from the current event, either spatially (higher-order node information) or along the time dimension (events occurred in the past). To address long-range dependencies, we introduce Continuous-Time Graph Anti-Symmetric Network (CTAN). Grounded within the ordinary differential equations framework, our method is designed for efficient propagation of information. In this paper, we show how CTAN's (i) long-range modeling capabilities are substantiated by theoretical findings and how (ii) its empirical performance on synthetic long-range benchmarks and real-world benchmarks is superior to other methods. Our results motivate CTAN's ability to propagate long-range information in C-TDGs as well as the inclusion of long-range tasks as part of temporal graph models evaluation.<|reference_end|>
@article{gravina2024long,
    title={Long Range Propagation on Continuous-Time Dynamic Graphs},
    author={Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio, Davide Bacciu,
  Claas Grohnfeldt},
    journal={arXiv preprint arXiv:2406.02740},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02740},
    primaryClass={cs.LG}
}