{"type":"ScholarlyArticle","title":"Graph Edit Distance Learning via Modeling Optimum Matchings with Constraints","authors":[{"name":"Yun Peng","slug":"yun-peng","given":"Yun","family":"Peng","affiliation":""},{"name":"Byron Choi","slug":"byron-choi","given":"Byron","family":"Choi","affiliation":""},{"name":"Jianliang Xu","slug":"jianliang-xu","given":"Jianliang","family":"Xu","affiliation":""}],"abstract":"Graph edit distance (GED) is a fundamental measure for graph similarity analysis in many real applications. GED computation has known to be NP-hard and many heuristic methods are proposed. GED has two inherent characteristics: multiple optimum node matchings and one-to-one node matching constraints. However, these two characteristics have not been well considered in the existing learning-based methods, which leads to suboptimal models. In this paper, we propose a novel GED-specific loss function that simultaneously encodes the two characteristics. First, we propose an optimal partial node matching-based regularizer to encode multiple optimum node matchings. Second, we propose a plane intersection-based regularizer to impose the one-to-one constraints for the encoded node matchings. We use the graph neural network on the association graph of the two input graphs to learn the cross-graph representation. Our experiments show that our method is 4.2x-103.8x more accurate than the state-of-the-art methods on real-world benchmark graphs.","doi":"10.24963/ijcai.2021/212","url":"https://ijcai.org/proceedings/2021/212","pdf":"https://ijcai.org/proceedings/2021/0212.pdf","markdown":"https://ijcai.org/proceedings/2021/212.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/212","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Data Mining","track":"Main Track","number":212,"pages":{"first":1534,"last":1540},"keywords":["Data Mining: Big Data, Large-Scale Systems","Data Mining: Intelligent Database Systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}