{"created":"2025-01-19T01:12:52.665720+00:00","updated":"2025-01-19T17:41:52.525504+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211740","sets":["1164:2735:10526:10613"]},"path":["10613"],"owner":"44499","recid":"211740","title":["属性区間付きグラフを用いた予測グラフマイニング"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-06-21"},"_buckets":{"deposit":"1ac7e7df-239d-4e5a-beb7-8922daf93372"},"_deposit":{"id":"211740","pid":{"type":"depid","value":"211740","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"属性区間付きグラフを用いた予測グラフマイニング","author_link":["538480","538482","538479","538481"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"属性区間付きグラフを用いた予測グラフマイニング"},{"subitem_title":"Predictive Graph Mining using Graphs with Interval Attributes","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"自動運転・学習理論","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-06-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"名古屋工業大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/211740/files/IPSJ-MPS21133006.pdf","label":"IPSJ-MPS21133006.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS21133006.pdf","filesize":[{"value":"2.0 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"94dbc03b-9fc6-4e05-9e3d-20e399948ecb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"朝日, 陽向"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"烏山, 昌幸"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hinata, Asahi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masayuki, Karasuyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","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":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"分子構造や交通ネットワークなど複雑な構造化データを表現する方法としてグラフが広く用いられている． 本研究では，各頂点または辺が連続値を持つ属性付きグラフから，予測に寄与する部分グラフを抽出する予測グラフマイニングを考える．既存の予測グラフマイニングアルゴリズムは属性値が離散の場合のみを扱っており，連続値の属性付きグラフから解釈可能な表現を抽出することはできなかった．ここでは，属性値の区間が付随した部分グラフ（属性区間付きグラフ）によって，どのような属性値を持つ部分グラフが予測に寄与するのか表現し，スパースモデルにより重要な属性区間付き部分グラフを発見する方法を提案する．あり得る属性区間付き部分グラフは膨大だが，予  測へ寄与しない特徴量を枝刈りするスクリーニングと，区間と部分グラフの列挙を同時に行うマイニング木を組み合わせることで効率的な最適化が可能になることを示す．さらに，計算機実験で既存手法との比較を行い，提案法の有用性を示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Graphs have been widely used to represent structured data such as molecular data and traffic networks. In this paper, we consider a predictive graph mining problem for continuous attributed graphs, while existing predictive graph mining methods are only for discrete attributes. We employ an approach based on a graph with ‘intervals of attributes’, which we call an interval-attributed graph. This enables to extract interpretable representations from continuous attributed graphs. We propose a sparse linear model by which we can identify a small number of important interval-attributed subgraphs for the prediction. Although there exist a large number of possible interval-attributed subgraphs, we show that an efficient pruning method can be constructed by using a mining tree that enumerates both of subgraphs and intervals. Furthermore, we compare our proposed method with existing methods by using several benchmark datasets.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-06-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"2021-MPS-133"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":211740,"links":{}}