{"id":199764,"updated":"2025-01-19T21:32:48.738456+00:00","links":{},"created":"2025-01-19T01:03:38.341167+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00199764","sets":["934:1022:9621:9893"]},"path":["9893"],"owner":"44499","recid":"199764","title":["Time Series Link Prediction Using NMF"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-10-23"},"_buckets":{"deposit":"f37ae558-ff1c-4e49-b760-51a558f9f891"},"_deposit":{"id":"199764","pid":{"type":"depid","value":"199764","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Time Series Link Prediction Using NMF","author_link":["484577","484576","484578","484579","484572","484580","484573","484575","484571","484574"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Time Series Link Prediction Using NMF"},{"subitem_title":"Time Series Link Prediction Using NMF","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[研究論文] link prediction, forecasting, non-negative matrix factorization (NMF)","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2019-10-23","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"／Nara Institute of Science and Technology"},{"subitem_text_value":"IBM"},{"subitem_text_value":"NTT Communication Science Laboratories"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":" / Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"IBM","subitem_text_language":"en"},{"subitem_text_value":"NTT Communication Science Laboratories","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","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/199764/files/IPSJ-TOD1204011.pdf","label":"IPSJ-TOD1204011.pdf"},"date":[{"dateType":"Available","dateValue":"2021-10-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOD1204011.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c4644b70-3a6a-4494-a5f3-1dff5ce67f03","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Faith, Mutinda"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsuhiro, Nakashima"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koh, Takeuchi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuya, Sasaki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Makoto, Onizuka"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Faith, Mutinda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsuhiro, Nakashima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koh, Takeuchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuya, Sasaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Makoto, Onizuka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464847","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_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7799","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Data in many fields such as e-commerce, social networks, and web data can be modeled as bipartite graphs, where a node represents a person and/or an object and a link represents the relationship between people and/or objects. Since the relationships change with time, data mining techniques for time series graphs have been actively studied. In this paper, we study the problem of predicting links in the future graph from historical graphs. Although various studies have been carried out on link prediction, the prediction accuracy of existing methods is still low because it is difficult to capture continuous change with time. Therefore, we propose a new method that combines non-negative matrix factorization (NMF) and a time series data forecasting method. NMF extracts the latent features while the forecasting method captures and predicts the changes of the features with time. Our method can predict hidden links that do not appear in historical graphs. Our experiments with real datasets show that our method has a higher prediction accuracy compared to existing methods.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.27(2019) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Data in many fields such as e-commerce, social networks, and web data can be modeled as bipartite graphs, where a node represents a person and/or an object and a link represents the relationship between people and/or objects. Since the relationships change with time, data mining techniques for time series graphs have been actively studied. In this paper, we study the problem of predicting links in the future graph from historical graphs. Although various studies have been carried out on link prediction, the prediction accuracy of existing methods is still low because it is difficult to capture continuous change with time. Therefore, we propose a new method that combines non-negative matrix factorization (NMF) and a time series data forecasting method. NMF extracts the latent features while the forecasting method captures and predicts the changes of the features with time. Our method can predict hidden links that do not appear in historical graphs. Our experiments with real datasets show that our method has a higher prediction accuracy compared to existing methods.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.27(2019) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌データベース（TOD）"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2019-10-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"12"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}