Link Prediction in Sparse Networks by Incidence Matrix Factorization

• Item type: Journal(1)
• 公開日: 2017-07-15
• タイトル: Link Prediction in Sparse Networks by Incidence Matrix Factorization
• タイトル / 言語: en
• タイトル: Link Prediction in Sparse Networks by Incidence Matrix Factorization
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] link prediction, data sparsity problem, matrix factorization, incidence matrix
• 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: Tohoku University
• 著者所属: IBM Research - Tokyo
• 著者所属: Kyoto University
• 著者所属(英): en
• 著者所属(英): Tohoku University
• 著者所属(英): en
• 著者所属(英): IBM Research - Tokyo
• 著者所属(英): en
• 著者所属(英): Kyoto University
• 著者名: Sho, Yokoi; Hiroshi, Kajino; Hisashi, Kashima
• 著者名(英): Sho, Yokoi; Hiroshi, Kajino; Hisashi, Kashima
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Link prediction plays an important role in multiple areas of artificial intelligence, including social network analysis and bioinformatics; however, it is often negatively affected by the data sparsity problem. In this paper, we present and validate our hypothesis, i.e., for sparse networks, incidence matrix factorization (IMF) could perform better than adjacency matrix factorization (AMF), the latter used in many previous studies. A key observation supporting our hypothesis here is that IMF models a partially observed graph more accurately than AMF. Unfortunately, a technical challenge we face in validating our hypothesis is that there is not an obvious method for making link prediction using a factorized incidence matrix, unlike the AMF approach. To this end, we developed an optimization-based link prediction method. Then we have conducted thorough experiments using both synthetic and real-world datasets to investigate the relationship between the sparsity of a network and the predictive performance of the aforementioned two factorization approaches. Our experimental results show that IMF performed better than AMF as networks became sparser, which validates our hypothesis.; ------------------------------; 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.25(2017) (online); DOI http://dx.doi.org/10.2197/ipsjjip.25.477; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Link prediction plays an important role in multiple areas of artificial intelligence, including social network analysis and bioinformatics; however, it is often negatively affected by the data sparsity problem. In this paper, we present and validate our hypothesis, i.e., for sparse networks, incidence matrix factorization (IMF) could perform better than adjacency matrix factorization (AMF), the latter used in many previous studies. A key observation supporting our hypothesis here is that IMF models a partially observed graph more accurately than AMF. Unfortunately, a technical challenge we face in validating our hypothesis is that there is not an obvious method for making link prediction using a factorized incidence matrix, unlike the AMF approach. To this end, we developed an optimization-based link prediction method. Then we have conducted thorough experiments using both synthetic and real-world datasets to investigate the relationship between the sparsity of a network and the predictive performance of the aforementioned two factorization approaches. Our experimental results show that IMF performed better than AMF as networks became sparser, which validates our hypothesis.; ------------------------------; 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.25(2017) (online); DOI http://dx.doi.org/10.2197/ipsjjip.25.477; ------------------------------
• 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: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 58, 号 7, 発行日 2017-07-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764