{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/long-range-propagation-on-continuous-time","title":"Long Range Propagation on Continuous-Time Dynamic Graphs","arxiv_id":"2406.02740","date":"2024-06-04","proceeding":null,"authors":["Alessio Gravina","Giulio Lovisotto","Claudio Gallicchio","Davide Bacciu","Claas Grohnfeldt"],"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.","url_abs":"https://arxiv.org/abs/2406.02740v1","url_pdf":"https://arxiv.org/pdf/2406.02740v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"long-range-propagation-on-continuous-time","repo_url":"https://github.com/gravins/non-dissipative-propagation-CTDGs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"long-range-modeling","task_name":"Long-range modeling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.02740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02740"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/gravins/non-dissipative-propagation-CTDGs","reach":null}],"summary":{"ran":1,"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"e95a36ecfd64604b","entry":"GenericModel","repo":"gravins/non-dissipative-propagation-CTDGs","repo_kind":"official","path":"models/ctdg_models.py","file_url":"https://github.com/gravins/non-dissipative-propagation-CTDGs/blob/HEAD/models/ctdg_models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"e95a36ecfd64604b"}},{"code_sha256_prefix":"c21c2f155065170c","entry":"aggregate_res","repo":"gravins/non-dissipative-propagation-ctdgs","repo_kind":"official","path":"main_tgb_ctan.py","file_url":"https://github.com/gravins/non-dissipative-propagation-ctdgs/blob/HEAD/main_tgb_ctan.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"c21c2f155065170c"}},{"code_sha256_prefix":"3506c74436204d6e","entry":"compute_row","repo":"gravins/non-dissipative-propagation-ctdgs","repo_kind":"official","path":"main_tgb_ctan.py","file_url":"https://github.com/gravins/non-dissipative-propagation-ctdgs/blob/HEAD/main_tgb_ctan.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"3506c74436204d6e"}},{"code_sha256_prefix":"b1d782a58a37a3a7","entry":"CTAN","repo":"gravins/non-dissipative-propagation-CTDGs","repo_kind":"official","path":"models/ctdg_models.py","file_url":"https://github.com/gravins/non-dissipative-propagation-CTDGs/blob/HEAD/models/ctdg_models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"b1d782a58a37a3a7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}