{"created":"2025-01-19T01:09:38.673230+00:00","updated":"2025-01-19T18:56:54.889646+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00208045","sets":["1164:3616:10147:10408"]},"path":["10408"],"owner":"44499","recid":"208045","title":["A Wasserstein Graph Kernel based on Substructure Isomorphism Problem of Shortest Paths"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-11-19"},"_buckets":{"deposit":"06a49a6d-4c3c-4781-a54c-1914dceba50f"},"_deposit":{"id":"208045","pid":{"type":"depid","value":"208045","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"A Wasserstein Graph Kernel based on Substructure Isomorphism Problem of Shortest Paths","author_link":["520487","520485","520486","520489","520490","520488"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"A Wasserstein Graph Kernel based on Substructure Isomorphism Problem of Shortest Paths"},{"subitem_title":"A Wasserstein Graph Kernel based on Substructure Isomorphism Problem of Shortest Paths","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"学生セッション","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2020-11-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"WASEDA University, Graduate School of Fundamental Science and Engineering"},{"subitem_text_value":"WASEDA University, School of Fundamental Science and Engineering, Dept. of Communications and Computer Engineering"},{"subitem_text_value":"WASEDA University, School of Fundamental Science and Engineering, Dept. of Communications and Computer Engineering"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"WASEDA University, Graduate School of Fundamental Science and Engineering","subitem_text_language":"en"},{"subitem_text_value":"WASEDA University, School of Fundamental Science and Engineering, Dept. of Communications and Computer Engineering","subitem_text_language":"en"},{"subitem_text_value":"WASEDA University, School of Fundamental Science and Engineering, Dept. of Communications and Computer Engineering","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/208045/files/IPSJ-AVM20111005.pdf","label":"IPSJ-AVM20111005.pdf"},"date":[{"dateType":"Available","dateValue":"2022-11-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AVM20111005.pdf","filesize":[{"value":"657.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"27"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5f48273c-b500-4c2b-8f60-d18ad8ff0c27","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jianming, Huang"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Zhongxi, Fang"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Kasai"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jianming, Huang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Zhongxi, Fang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Kasai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438399","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8582","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"For graph classification tasks, many methods use a common strategy to aggregate information of vertex neighbors. Although this strategy provides an efficient means of extracting graph topological features, it brings excessive amounts of information that might greatly reduce its accuracy when dealing with large-scale neighborhoods. Learning graphs using paths or walks will not suffer from this difficulty, but many have low utilization of each path or walk, which might engender information loss and high computational costs. To solve this, we propose a graph kernel using a longest common subsequence (LCS kernel) to compute more comprehensive similarity between paths and walks, which resolves substructure isomorphism difficulties. We also combine it with optimal transport theory to extract more in-depth features of graphs.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"For graph classification tasks, many methods use a common strategy to aggregate information of vertex neighbors. Although this strategy provides an efficient means of extracting graph topological features, it brings excessive amounts of information that might greatly reduce its accuracy when dealing with large-scale neighborhoods. Learning graphs using paths or walks will not suffer from this difficulty, but many have low utilization of each path or walk, which might engender information loss and high computational costs. To solve this, we propose a graph kernel using a longest common subsequence (LCS kernel) to compute more comprehensive similarity between paths and walks, which resolves substructure isomorphism difficulties. We also combine it with optimal transport theory to extract more in-depth features of graphs.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"研究報告オーディオビジュアル複合情報処理（AVM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2020-11-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2020-AVM-111"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":208045,"links":{}}