{"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/gets-ensemble-temperature-scaling-for","title":"GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks","arxiv_id":"2410.09570","date":"2024-10-12","proceeding":null,"authors":["Dingyi Zhuang","Chonghe Jiang","Yunhan Zheng","Shenhao Wang","Jinhua Zhao"],"abstract":"Graph Neural Networks deliver strong classification results but often suffer from poor calibration performance, leading to overconfidence or underconfidence. This is particularly problematic in high stakes applications where accurate uncertainty estimates are essential. Existing post hoc methods, such as temperature scaling, fail to effectively utilize graph structures, while current GNN calibration methods often overlook the potential of leveraging diverse input information and model ensembles jointly. In the paper, we propose Graph Ensemble Temperature Scaling, a novel calibration framework that combines input and model ensemble strategies within a Graph Mixture of Experts archi SOTA calibration techniques, reducing expected calibration error by 25 percent across 10 GNN benchmark datasets. Additionally, GETS is computationally efficient, scalable, and capable of selecting effective input combinations for improved calibration performance.","url_abs":"https://arxiv.org/abs/2410.09570v1","url_pdf":"https://arxiv.org/pdf/2410.09570v1.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":[],"tasks":[{"task_slug":"mixture-of-experts","task_name":"Mixture-of-Experts"}],"methods":[{"method_slug":"hoc","method_name":"HOC"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.09570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09570"}},"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/ZhuangDingyi/GETS","reach":{"status":"ok"}}],"summary":{"ran":3},"by_repo_kind":{"found_in_text":{"samples":3,"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":3,"samples":[{"code_sha256_prefix":"95c592d5e06b1e55","entry":"accuracy","repo":"ZhuangDingyi/GETS","repo_kind":"found_in_text","path":"utils/utils.py","file_url":"https://github.com/ZhuangDingyi/GETS/blob/HEAD/utils/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":"95c592d5e06b1e55"}},{"code_sha256_prefix":"d312c51668dc693f","entry":"bfs_subgraph","repo":"ZhuangDingyi/GETS","repo_kind":"found_in_text","path":"visualize.py","file_url":"https://github.com/ZhuangDingyi/GETS/blob/HEAD/visualize.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":"d312c51668dc693f"}},{"code_sha256_prefix":"9077fbac88b366a4","entry":"shortest_path_length","repo":"ZhuangDingyi/GETS","repo_kind":"found_in_text","path":"model/calibrator.py","file_url":"https://github.com/ZhuangDingyi/GETS/blob/HEAD/model/calibrator.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":"9077fbac88b366a4"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}