Long Range Propagation on Continuous-Time Dynamic Graphs

• URL: http://arxiv.org/abs/2406.02740v1
• Date: Tue, 4 Jun 2024 19:42:19 GMT
• Title: Long Range Propagation on Continuous-Time Dynamic Graphs
• Authors: Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio, Davide Bacciu, Claas Grohnfeldt,
• Abstract summary: Continuous-Time Graph Anti-Symmetric Network (CTAN) is designed for efficient propagation of information. We show how CTAN's empirical performance on synthetic long-range benchmarks and real-world benchmarks is superior to other methods.
• Score: 18.5534584418248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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