{"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/combinatorial-optimization-with-graph","title":"Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search","arxiv_id":"1810.10659","date":"2018-10-25","proceeding":"NeurIPS 2018 12","authors":["Zhuwen Li","Qifeng Chen","Vladlen Koltun"],"abstract":"We present a learning-based approach to computing solutions for certain\nNP-hard problems. Our approach combines deep learning techniques with useful\nalgorithmic elements from classic heuristics. The central component is a graph\nconvolutional network that is trained to estimate the likelihood, for each\nvertex in a graph, of whether this vertex is part of the optimal solution. The\nnetwork is designed and trained to synthesize a diverse set of solutions, which\nenables rapid exploration of the solution space via tree search. The presented\napproach is evaluated on four canonical NP-hard problems and five datasets,\nwhich include benchmark satisfiability problems and real social network graphs\nwith up to a hundred thousand nodes. Experimental results demonstrate that the\npresented approach substantially outperforms recent deep learning work, and\nperforms on par with highly optimized state-of-the-art heuristic solvers for\nsome NP-hard problems. Experiments indicate that our approach generalizes\nacross datasets, and scales to graphs that are orders of magnitude larger than\nthose used during training.","url_abs":"http://arxiv.org/abs/1810.10659v1","url_pdf":"http://arxiv.org/pdf/1810.10659v1.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":"combinatorial-optimization-with-graph","repo_url":"https://github.com/abi-kothapalli/minimumdominatingsets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"combinatorial-optimization-with-graph","repo_url":"https://github.com/maxiboether/mis-benchmark-framework","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"combinatorial-optimization","task_name":"Combinatorial Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.10659","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}