{"created":"2025-01-19T01:15:46.754449+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214991","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"214991","title":["適応的なノード埋め込みの伝搬による半教師ありノード分類モデル"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"91dfaa36-8d34-49b0-bb85-6214b7dfda2a"},"_deposit":{"id":"214991","pid":{"type":"depid","value":"214991","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"適応的なノード埋め込みの伝搬による半教師ありノード分類モデル","author_link":["553301","553303","553304","553300","553302"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"適応的なノード埋め込みの伝搬による半教師ありノード分類モデル"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"NTT"},{"subitem_text_value":"阪大"}]},"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/214991/files/IPSJ-Z83-4Q-05.pdf","label":"IPSJ-Z83-4Q-05.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-4Q-05.pdf","filesize":[{"value":"326.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"e23025e3-19b2-484c-a1cb-914e2996bc3f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小川, 裕也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"前川, 政司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐々木, 勇和"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤原, 靖宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鬼塚, 真"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"半教師ありノード分類においてグラフ畳み込みネットワーク(GCN)は埋め込みをグラフ上で伝搬する技術である．しかし，既存のGCNは層の数が固定されているため，実世界のグラフの多様性を無視する．また，層が浅いため埋め込みをグラフ全体に伝搬することができないが，層が深いと過剰平滑化により精度が下がることが知られている．そこで本研究では適応的なノード埋め込み伝搬ネットワークを提案する．提案手法はクラス間分散とクラス内分散に基づいて適応的に伝搬を制御する．また，過剰平滑化を防ぎながら広範囲に埋め込みを伝搬する．評価実験において提案手法が既存手法の分類精度を上回ることを示した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"382","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"381","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"links":{},"id":214991,"updated":"2025-01-19T16:22:19.936245+00:00"}