{"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/learning-divergence-fields-for-shift-robust","title":"Learning Divergence Fields for Shift-Robust Graph Representations","arxiv_id":"2406.04963","date":"2024-06-07","proceeding":null,"authors":["Qitian Wu","Fan Nie","Chenxiao Yang","Junchi Yan"],"abstract":"Real-world data generation often involves certain geometries (e.g., graphs) that induce instance-level interdependence. This characteristic makes the generalization of learning models more difficult due to the intricate interdependent patterns that impact data-generative distributions and can vary from training to testing. In this work, we propose a geometric diffusion model with learnable divergence fields for the challenging generalization problem with interdependent data. We generalize the diffusion equation with stochastic diffusivity at each time step, which aims to capture the multi-faceted information flows among interdependent data. Furthermore, we derive a new learning objective through causal inference, which can guide the model to learn generalizable patterns of interdependence that are insensitive across domains. Regarding practical implementation, we introduce three model instantiations that can be considered as the generalized versions of GCN, GAT, and Transformers, respectively, which possess advanced robustness against distribution shifts. We demonstrate their promising efficacy for out-of-distribution generalization on diverse real-world datasets.","url_abs":"https://arxiv.org/abs/2406.04963v1","url_pdf":"https://arxiv.org/pdf/2406.04963v1.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":"learning-divergence-fields-for-shift-robust","repo_url":"https://github.com/fannie1208/glind","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"out-of-distribution-generalization","task_name":"Out-of-Distribution Generalization"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"gat","method_name":"GAT"},{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.04963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04963"}},"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/fannie1208/GLIND","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/fannie1208/glind","reach":{"status":"ok"}}],"summary":{"ran":5,"unverified":3},"by_repo_kind":{"official":{"samples":8,"ran":5,"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":8,"samples":[{"code_sha256_prefix":"137fd20bed7ac88a","entry":"batch_repeat","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/difformer.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/difformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"137fd20bed7ac88a"}},{"code_sha256_prefix":"8af7081a7caceae1","entry":"load_fixed_splits","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/data_utils.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8af7081a7caceae1"}},{"code_sha256_prefix":"f7d2020636698e09","entry":"rand_splits","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/data_utils.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f7d2020636698e09"}},{"code_sha256_prefix":"02289063bb2416ef","entry":"to_block","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/difformer.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/difformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"02289063bb2416ef"}},{"code_sha256_prefix":"e87d8f83c5841328","entry":"unpack_block","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/difformer.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/difformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e87d8f83c5841328"}},{"code_sha256_prefix":"711b5792cfdf73cf","entry":"even_quantile_labels","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/data_utils.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"711b5792cfdf73cf"}},{"code_sha256_prefix":"2cf31b6d1b1db7fc","entry":"load_arxiv_dataset","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/dataset.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2cf31b6d1b1db7fc"}},{"code_sha256_prefix":"345bcfffc570655c","entry":"load_twitch_dataset","repo":"fannie1208/GLIND","repo_kind":"official","path":"observed/dataset.py","file_url":"https://github.com/fannie1208/GLIND/blob/HEAD/observed/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"345bcfffc570655c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}