{"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/spatial-temporal-graph-learning-with","title":"Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation","arxiv_id":"2306.10683","date":"2023-06-19","proceeding":null,"authors":["Qianru Zhang","Chao Huang","Lianghao Xia","Zheng Wang","SiuMing Yiu","Ruihua Han"],"abstract":"Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing tasks such as crime forecasting and traffic flow prediction. However, most existing models are vulnerable to the quality of the generated region graph due to the inaccurate graph-structured information aggregation schema. The ubiquitous spatial-temporal data noise and incompleteness in real-life scenarios pose challenges in generating high-quality region representations. To address this challenge, we propose a new spatial-temporal graph learning model (GraphST) for enabling effective self-supervised learning. Our proposed model is an adversarial contrastive learning paradigm that automates the distillation of crucial multi-view self-supervised information for robust spatial-temporal graph augmentation. We empower GraphST to adaptively identify hard samples for better self-supervision, enhancing the representation discrimination ability and robustness. In addition, we introduce a cross-view contrastive learning paradigm to model the inter-dependencies across view-specific region representations and preserve underlying relation heterogeneity. We demonstrate the superiority of our proposed GraphST method in various spatial-temporal prediction tasks on real-life datasets. We release our model implementation via the link: \\url{https://github.com/HKUDS/GraphST}.","url_abs":"https://arxiv.org/abs/2306.10683v1","url_pdf":"https://arxiv.org/pdf/2306.10683v1.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":"spatial-temporal-graph-learning-with","repo_url":"https://github.com/hkuds/graphst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2306.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10683"}},"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":"deterministic:regex_extraction","url":"https://github.com/HKUDS/GraphST","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hkuds/graphst","reach":null}],"summary":{"ran":2,"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":4,"ran":4,"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":4,"samples":[{"code_sha256_prefix":"514f39ef74a3ae0d","entry":"Encoder","repo":"hkuds/graphst","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hkuds/graphst/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"514f39ef74a3ae0d"}},{"code_sha256_prefix":"ec0cf0a890ed9a98","entry":"Model","repo":"hkuds/graphst","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hkuds/graphst/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ec0cf0a890ed9a98"}},{"code_sha256_prefix":"22bfb6129463d322","entry":"drop_feature","repo":"HKUDS/GraphST","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/HKUDS/GraphST/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"22bfb6129463d322"}},{"code_sha256_prefix":"de0879d0a5da3a1b","entry":"load_data","repo":"HKUDS/GraphST","repo_kind":"official","path":"code/train_edit_auto.py","file_url":"https://github.com/HKUDS/GraphST/blob/HEAD/code/train_edit_auto.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"de0879d0a5da3a1b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}