{"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/a-graph-is-more-than-its-nodes-towards","title":"A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs","arxiv_id":"2210.15575","date":"2022-10-27","proceeding":null,"authors":["Hans Hao-Hsun Hsu","Yuesong Shen","Daniel Cremers"],"abstract":"Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics. This limits uncertainty estimation on graphs since nodewise marginals do not fully characterize the joint distribution given the graph structure. In this work, we propose novel edgewise metrics, namely the edgewise expected calibration error (ECE) and the agree/disagree ECEs, which provide criteria for uncertainty estimation on graphs beyond the nodewise setting. Our experiments demonstrate that the proposed edgewise metrics can complement the nodewise results and yield additional insights. Moreover, we show that GNN models which consider the structured prediction problem on graphs tend to have better uncertainty estimations, which illustrates the benefit of going beyond the nodewise setting.","url_abs":"https://arxiv.org/abs/2210.15575v1","url_pdf":"https://arxiv.org/pdf/2210.15575v1.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":"a-graph-is-more-than-its-nodes-towards","repo_url":"https://github.com/hans66hsu/structured_uncertainty_metrics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.15575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15575"}},"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/hans66hsu/structured_uncertainty_metrics","reach":null}],"summary":{"ran":3,"ran_fixture":2,"unverified":1},"by_repo_kind":{"official":{"samples":6,"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":0,"samples":[{"code_sha256_prefix":"a161c5cfbb1c7ed2","entry":"ECE","repo":"hans66hsu/structured_uncertainty_metrics","repo_kind":"official","path":"src/calibloss.py","file_url":"https://github.com/hans66hsu/structured_uncertainty_metrics/blob/HEAD/src/calibloss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a161c5cfbb1c7ed2"}},{"code_sha256_prefix":"6115863ca2e00949","entry":"EdgewiseECE","repo":"hans66hsu/structured_uncertainty_metrics","repo_kind":"official","path":"src/calibloss.py","file_url":"https://github.com/hans66hsu/structured_uncertainty_metrics/blob/HEAD/src/calibloss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6115863ca2e00949"}},{"code_sha256_prefix":"c9216e1dab899fc5","entry":"Reliability","repo":"hans66hsu/structured_uncertainty_metrics","repo_kind":"official","path":"src/calibloss.py","file_url":"https://github.com/hans66hsu/structured_uncertainty_metrics/blob/HEAD/src/calibloss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c9216e1dab899fc5"}},{"code_sha256_prefix":"70b486d8c4c102bb","entry":"edge_conf","repo":"hans66hsu/structured_uncertainty_metrics","repo_kind":"official","path":"src/calibloss.py","file_url":"https://github.com/hans66hsu/structured_uncertainty_metrics/blob/HEAD/src/calibloss.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"70b486d8c4c102bb"}},{"code_sha256_prefix":"dd7dae932e8116b6","entry":"partial_sums","repo":"hans66hsu/structured_uncertainty_metrics","repo_kind":"official","path":"src/calibloss.py","file_url":"https://github.com/hans66hsu/structured_uncertainty_metrics/blob/HEAD/src/calibloss.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dd7dae932e8116b6"}},{"code_sha256_prefix":"351c6228b3e903a5","entry":"EdgewiseBase","repo":"hans66hsu/structured_uncertainty_metrics","repo_kind":"official","path":"src/calibloss.py","file_url":"https://github.com/hans66hsu/structured_uncertainty_metrics/blob/HEAD/src/calibloss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"351c6228b3e903a5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}