{"updated":"2025-01-23T03:21:44.921659+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00009572","sets":["581:586:593"]},"path":["593"],"owner":"1","recid":"9572","title":["リンクに基づく分類のためのネットワーク構造を用いた属性生成"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-06-15"},"_buckets":{"deposit":"f301ac30-3175-45b4-b007-6082a76297eb"},"_deposit":{"id":"9572","pid":{"type":"depid","value":"9572","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"リンクに基づく分類のためのネットワーク構造を用いた属性生成","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"リンクに基づく分類のためのネットワーク構造を用いた属性生成"},{"subitem_title":"Generating Social Network Features for Link-based Classification","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"一般論文","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2008-06-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"東京大学大学院工学系研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information and Technology, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Garduate School of Engineering, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information and Technology, The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/9572/files/IPSJ-JNL4906042.pdf"},"date":[{"dateType":"Available","dateValue":"2010-06-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL4906042.pdf","filesize":[{"value":"324.7 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"ac4a15bc-1ffc-4e0a-ae30-ae06e6d757d1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2008 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"唐門, 準"},{"creatorName":"松尾, 豊"},{"creatorName":"石塚, 満"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jun, Karamon","creatorNameLang":"en"},{"creatorName":"Yutaka, Matsuo","creatorNameLang":"en"},{"creatorName":"Mitsuru, Ishizuka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，ネットワーク構造を持つデータを用いて学習や予測を行うためのさまざまな研究が行われている．ソーシャルネットワークや遺伝子のネットワークなど，ネットワーク構造を持つデータは多く，ネットワークからのデータマイニングは一般にリンクマイニングと呼ばれる．その中でも，リンクが張られている近傍ノードの情報も利用しながらノードの分類を行うタスクは「リンクに基づく分類」（link-based classification）と呼ばれ，その精度を上げるためにネットワーク構造を用いたさまざまな指標が考案されている．一方，これまで社会ネットワーク分析や複雑ネットワークの分野ではネットワークを評価する指標として，中心性，構造空隙，クラスタ係数などがよく用いられた．本稿では，この2 つの研究の流れに注目し，従来から用いられてきた指標の生成を可能とするオペレータを定義し，リンクに基づく分類に適用する．論文のネットワークとソーシャルネットワークという2 種類のデータに適用し，従来から用いられてきた指標の重要性を明らかにするとともに，未知の指標の可能性についても議論する．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"There have been numerous attempts at the aggregation of attributes for relational data mining. Recently, an increasing number of studies have been undertaken to process social network data, partly because of the fact that so much social network data has become available. Among the various tasks in link mining, a popular task is <i>link-based classification</i>, by which samples are classified using the relations or links that are present among them. On the other hand, we sometimes employ traditional analytical methods in the field of social network analysis using e.g., centrality measures, structural holes, and network clustering. Through this study, we seek to bridge the gap between the aggregated features from the network data and traditional indices used in social network analysis. The notable feature of our algorithm is the ability to invent several indices that are well studied in sociology. We first define general operators that are applicable to an adjacent network. Then the combinations of the operators generate new features, some of which correspond to traditional indices, and others which are considered to be new. We apply our method for classification to two different datasets, thereby demonstrating the effectiveness of our approach.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2223","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"2212","bibliographicIssueDates":{"bibliographicIssueDate":"2008-06-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"49"}]},"relation_version_is_last":true,"item_2_alternative_title_2":{"attribute_name":"その他タイトル","attribute_value_mlt":[{"subitem_alternative_title":"データマイニング"}]},"weko_creator_id":"1"},"created":"2025-01-18T22:44:45.181382+00:00","id":9572,"links":{}}