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We thus call this formulation Semi-Supervised\nLearning with Heterophily (SSLH) and show how it generalizes and improves upon\na recently proposed approach called Linearized Belief Propagation (LinBP).\nImportantly, our framework allows us to reduce the problem of estimating the\nrelative compatibility between nodes from partially labeled graph to a simple\noptimization problem. The result is a very fast algorithm that -- despite its\nsimplicity -- is surprisingly effective: we can classify unlabeled nodes within\nthe same graph in the same time as LinBP but with a superior accuracy and\ndespite our algorithm not knowing the compatibilities.","url_abs":"http://arxiv.org/abs/1412.3100v2","url_pdf":"http://arxiv.org/pdf/1412.3100v2.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":"semi-supervised-learning-with-heterophily","repo_url":"https://github.com/northeastern-datalab/factorized-graphs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"semi-supervised-learning-with-heterophily","repo_url":"https://github.com/sslh/sslh","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.3100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1412.3100"}},"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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