Long Range Propagation on Continuous-Time Dynamic Graphs
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912270441775104 |
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| author | Gravina, Alessio Lovisotto, Giulio Gallicchio, Claudio Bacciu, Davide Grohnfeldt, Claas |
| author_facet | Gravina, Alessio Lovisotto, Giulio Gallicchio, Claudio Bacciu, Davide Grohnfeldt, Claas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Long Range Propagation on Continuous-Time Dynamic Graphs Gravina, Alessio Lovisotto, Giulio Gallicchio, Claudio Bacciu, Davide Grohnfeldt, Claas Machine Learning 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. |
| title | Long Range Propagation on Continuous-Time Dynamic Graphs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.02740 |