{"type":"ScholarlyArticle","title":"A Similarity Measurement Method Based on Graph Kernel for Disconnected Graphs","authors":[{"name":"Jun Gao","slug":"jun-gao","given":"Jun","family":"Gao","affiliation":"School of Computer Science and Engineering , Central South University , China"},{"name":"Jianliang Gao","slug":"jianliang-gao","given":"Jianliang","family":"Gao","affiliation":"School of Computer Science and Engineering , Central South University , China"}],"abstract":"Disconnected graphs are very common in the real world. However, most existing methods for graph similarity focus on connected graph. In this paper, we propose an effective approach for measuring the similarity of disconnected graphs. By embedding connected subgraphs with graph kernel, we obtain the feature vectors in low dimensional space. Then, we match the subgraphs and weigh the similarity of matched subgraphs. Finally, an intuitive example shows the feasibility of the method.","doi":"10.24963/ijcai.2019/904","url":"https://ijcai.org/proceedings/2019/904","pdf":"https://ijcai.org/proceedings/2019/0904.pdf","markdown":"https://ijcai.org/proceedings/2019/904.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/904","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":904,"pages":{"first":6430,"last":6431},"keywords":["Machine Learning Applications: Networks","Machine Learning: Kernel Methods"],"videos":[],"references":[{"position":1,"raw":"[Kondor and Pan, 2016] Risi Kondor and Horace Pan. The multiscale laplacian graph kernel. In Advances in Neural Information Processing Systems, pages 2990-2998, 2016.","title":"The multiscale laplacian graph kernel","authors":"Pan Kondor, Risi Kondor, Horace Pan","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Kriege et al., 2018] Nils M Kriege, Christopher Morris, An- ja Rey, and Christian Sohler. A property testing framework for the theoretical expressivity of graph kernels. In IJCAI, pages 2348-2354, 2018.","title":"A property testing framework for the theoretical expressivity of graph kernels","authors":"Christopher Nils M Kriege, Anja Morris, Christian Rey, Sohler","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/325"},{"position":3,"raw":"[Shervashidze et al., 2011] Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12(Sep):2539- 2561, 2011.","title":"Weisfeiler-lehman graph kernels","authors":"Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, Karsten M Borgwardt","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Stöcker et al., 2018] Bianca K Stöcker, Till Schäfer, Petra Mutzel, Johannes Köster, Nils Kriege, and Sven Rahman- n. Protein complex similarity based on weisfeiler-lehman labeling. Technical report, PeerJ Preprints, 2018.","title":"Protein complex similarity based on weisfeiler-lehman labeling","authors":"Till Bianca K Stöcker, Petra Schäfer, Johannes Mutzel, Nils Köster, Sven Kriege, Rahmann","year":"2018","venue":"PeerJ Preprints","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Yuan et al., 2019] Weiwei Yuan, Kangya He, Donghai Guan, Zhou Li, and Chenliang Li. Graph kernel based link prediction for signed social networks. Information Fusion, 46:1-10, 2019.","title":"Graph kernel based link prediction for signed social networks","authors":"Weiwei Yuan, Kangya He, Donghai Guan, Zhou Li, Chenliang Li","year":"2019","venue":"Information Fusion","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}