{"created":"2025-01-18T23:48:29.750176+00:00","updated":"2025-01-21T10:24:05.029815+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00104445","sets":["6504:7684:7685"]},"path":["7685"],"owner":"6748","recid":"104445","title":["多重有向コア抽出法によるTwitterデータの震災時と通常時の特性比較"],"pubdate":{"attribute_name":"公開日","attribute_value":"2014-03-11"},"_buckets":{"deposit":"e50dcf50-a3e5-4ea9-bd82-6ec5f2d3be21"},"_deposit":{"id":"104445","pid":{"type":"depid","value":"104445","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"多重有向コア抽出法によるTwitterデータの震災時と通常時の特性比較","author_link":["4620","4623","4621","4622"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"多重有向コア抽出法によるTwitterデータの震災時と通常時の特性比較"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ソフトウェア科学・工学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2014-03-11","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":"静岡県大"},{"subitem_text_value":"静岡県大"},{"subitem_text_value":"和歌山大"},{"subitem_text_value":"筑波大"}]},"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/104445/files/IPSJ-Z76-6L-3.pdf"},"date":[{"dateType":"Available","dateValue":"2014-10-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-Z76-6L-3.pdf","filesize":[{"value":"759.1 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"eec3a6fb-bf81-4d5a-9ea5-5eaa114baf81","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2014 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"加藤翔子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"斉藤和巳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"風間一洋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐藤哲司"}],"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":"本稿では，東日本大震災前後と定常時におけるTwitterのreplyデータを多重有向グラフ化し，コア抽出分解をおこなう．抽出手法には，単純無向グラフのコア抽出をおこなうSR(Spectral Relaxation)法を多重有向に拡張したMDSR(Multi-Directed-SR)法を用いる．MDSR法は，多重有向隣接行列の右固有ベクトルと左固有ベクトルを2値に量子化してコア部を抽出する．さらに，隣接行列から抽出したコア部に含まれるリンクを削除した後に上記の処理を適用し，再帰的にコア部を抽出する．右固有ベクトルはHITSアルゴリズムのAuthority度ベクトル，左固有ベクトルはHub度ベクトルに対応する．コア内のAuthorityノードとHubノードの比率の分布を見ることで，2つのデータのネットワーク構造を比較する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"410","bibliographic_titles":[{"bibliographic_title":"第76回全国大会講演論文集"}],"bibliographicPageStart":"409","bibliographicIssueDates":{"bibliographicIssueDate":"2014-03-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2014"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"id":104445,"links":{}}