{"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/constrained-generation-of-semantically-valid","title":"Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders","arxiv_id":"1809.02630","date":"2018-09-07","proceeding":"NeurIPS 2018 12","authors":["Tengfei Ma","Jie Chen","Cao Xiao"],"abstract":"Deep generative models have achieved remarkable success in various data\ndomains, including images, time series, and natural languages. There remain,\nhowever, substantial challenges for combinatorial structures, including graphs.\nOne of the key challenges lies in the difficulty of ensuring semantic validity\nin context. For examples, in molecular graphs, the number of bonding-electron\npairs must not exceed the valence of an atom; whereas in protein interaction\nnetworks, two proteins may be connected only when they belong to the same or\ncorrelated gene ontology terms. These constraints are not easy to be\nincorporated into a generative model. In this work, we propose a regularization\nframework for variational autoencoders as a step toward semantic validity. We\nfocus on the matrix representation of graphs and formulate penalty terms that\nregularize the output distribution of the decoder to encourage the satisfaction\nof validity constraints. Experimental results confirm a much higher likelihood\nof sampling valid graphs in our approach, compared with others reported in the\nliterature.","url_abs":"http://arxiv.org/abs/1809.02630v2","url_pdf":"http://arxiv.org/pdf/1809.02630v2.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":"constrained-generation-of-semantically-valid","repo_url":"https://github.com/Microsoft/constrained-graph-variational-autoencoder","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.02630"}},"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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