{"type":"ScholarlyArticle","title":"InfVC: An Inference-Enhanced Local Search Algorithm for the Minimum Vertex Cover Problem in Massive Graphs","authors":[{"name":"Rui Sun","slug":"rui-sun","given":"Rui","family":"Sun","affiliation":""},{"name":"Peiyan Liu","slug":"peiyan-liu","given":"Peiyan","family":"Liu","affiliation":""},{"name":"Yiyuan Wang","slug":"yiyuan-wang","given":"Yiyuan","family":"Wang","affiliation":""},{"name":"Zhaohui Liu","slug":"zhaohui-liu","given":"Zhaohui","family":"Liu","affiliation":""},{"name":"Liping Du","slug":"liping-du","given":"Liping","family":"Du","affiliation":""},{"name":"Jian Gao","slug":"jian-gao","given":"Jian","family":"Gao","affiliation":""}],"abstract":"The minimum vertex cover (MVC) problem is a classic NP-hard combinatorial optimization problem with extensive real-world applications. In this paper, we propose an efficient local search algorithm, InfVC, to solve the MVC in massive graphs, which comprises three ideas. First, we introduce an inference-driven optimization strategy that explores better feasible solutions through inference rules. Second, we develop a structural-determined perturbation strategy that is motivated by the structure features of high-quality solutions, prioritizing high-degree vertices into the candidate solution to guide the search process to some potential high-quality search area. Third, we design a self-adaptive local search framework that dynamically balances exploration and exploitation through a perturbation management mechanism. Extensive experiments demonstrate that InfVC outperforms all the state-of-the-art algorithms on almost massive instances.","doi":"10.24963/ijcai.2025/998","url":"https://ijcai.org/proceedings/2025/998","pdf":"https://ijcai.org/proceedings/2025/0998.pdf","markdown":"https://ijcai.org/proceedings/2025/998.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/998","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Search","track":"Main Track","number":998,"pages":{"first":8977,"last":8986},"keywords":["Search: S: Local search","Search: S: Heuristic search"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}