Time Series Link Prediction Using NMF

• Item type: Trans(1)
• 公開日: 2019-10-23
• タイトル: Time Series Link Prediction Using NMF
• タイトル / 言語: en
• タイトル: Time Series Link Prediction Using NMF
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [研究論文] link prediction, forecasting, non-negative matrix factorization (NMF)
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: ／Nara Institute of Science and Technology
• 著者所属: IBM
• 著者所属: NTT Communication Science Laboratories
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): / Nara Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): IBM
• 著者所属(英): en
• 著者所属(英): NTT Communication Science Laboratories
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者名: Faith, Mutinda; Atsuhiro, Nakashima; Koh, Takeuchi; Yuya, Sasaki; Makoto, Onizuka
• 著者名(英): Faith, Mutinda; Atsuhiro, Nakashima; Koh, Takeuchi; Yuya, Sasaki; Makoto, Onizuka
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 12, 号 4, 発行日 2019-10-23
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会