{"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/confidence-based-graph-convolutional-networks","title":"Confidence-based Graph Convolutional Networks for Semi-Supervised Learning","arxiv_id":"1901.08255","date":"2019-01-24","proceeding":null,"authors":["Shikhar Vashishth","Prateek Yadav","Manik Bhandari","Partha Talukdar"],"abstract":"Predicting properties of nodes in a graph is an important problem with\napplications in a variety of domains. Graph-based Semi-Supervised Learning\n(SSL) methods aim to address this problem by labeling a small subset of the\nnodes as seeds and then utilizing the graph structure to predict label scores\nfor the rest of the nodes in the graph. Recently, Graph Convolutional Networks\n(GCNs) have achieved impressive performance on the graph-based SSL task. In\naddition to label scores, it is also desirable to have confidence scores\nassociated with them. Unfortunately, confidence estimation in the context of\nGCN has not been previously explored. We fill this important gap in this paper\nand propose ConfGCN, which estimates labels scores along with their confidences\njointly in GCN-based setting. ConfGCN uses these estimated confidences to\ndetermine the influence of one node on another during neighborhood aggregation,\nthereby acquiring anisotropic capabilities. Through extensive analysis and\nexperiments on standard benchmarks, we find that ConfGCN is able to outperform\nstate-of-the-art baselines. We have made ConfGCN's source code available to\nencourage reproducible research.","url_abs":"http://arxiv.org/abs/1901.08255v2","url_pdf":"http://arxiv.org/pdf/1901.08255v2.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":"confidence-based-graph-convolutional-networks","repo_url":"https://github.com/malllabiisc/ConfGCN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.08255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08255"}},"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. 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