{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T07:33:48Z","timestamp":1791272028483,"version":"4.1.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2011,2,1]],"date-time":"2011-02-01T00:00:00Z","timestamp":1296518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC04-94AL85000"],"award-info":[{"award-number":["DE-AC04-94AL85000"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2011,2]]},"abstract":"<jats:p>\n                    The data in many disciplines such as social networks, Web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this article, we consider the problem of temporal link prediction: Given link data for times 1 through\n                    <jats:italic>T<\/jats:italic>\n                    , can we predict the links at time\n                    <jats:italic>T<\/jats:italic>\n                    + 1? If our data has underlying periodic structure, can we predict out even further in time, i.e., links at time\n                    <jats:italic>T<\/jats:italic>\n                    + 2,\n                    <jats:italic>T<\/jats:italic>\n                    + 3, etc.? In this article, we consider bipartite graphs that evolve over time and consider matrix- and tensor-based methods for predicting future links. We present a weight-based method for collapsing multiyear data into a single matrix. We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition. Using a CANDECOMP\/PARAFAC tensor decomposition of the data, we illustrate the usefulness of exploiting the natural three-dimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrix- and tensor-based techniques are effective for temporal link prediction despite the inherent difficulty of the problem. Additionally, we show that tensor-based techniques are particularly effective for temporal data with varying periodic patterns.\n                  <\/jats:p>","DOI":"10.1145\/1921632.1921636","type":"journal-article","created":{"date-parts":[[2011,3,3]],"date-time":"2011-03-03T03:44:26Z","timestamp":1299123866000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":371,"title":["Temporal Link Prediction Using Matrix and Tensor Factorizations"],"prefix":"10.1145","volume":"5","author":[{"given":"Daniel M.","family":"Dunlavy","sequence":"first","affiliation":[{"name":"Sandia National Laboratories"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamara G.","family":"Kolda","sequence":"additional","affiliation":[{"name":"Sandia National Laboratories"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evrim","family":"Acar","sequence":"additional","affiliation":[{"name":"National Research Institute of Electronics and Cryptology (TUBITAK-UEKAE)"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2011,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.112"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/11760146_19"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.54"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Acar E. Dunlavy D. M. and Kolda T. G. 2010. A scalable optimization approach for fitting canonical tensor decompositions. J. Chemometrics. To appear. Acar E. Dunlavy D. M. and Kolda T. G. 2010. A scalable optimization approach for fitting canonical tensor decompositions. J. Chemometrics . To appear.","DOI":"10.2172\/978916"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1137\/060676489"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Bader B. W. Berry M. W. and Browne M. 2007. Discussion tracking in Enron email using PARAFAC. In Survey of Text Mining: Clustering Classification and Retrieval 2nd Ed. M. W. Berry and M. Castellanos Eds. Springer 147--162. Bader B. W. Berry M. W. and Browne M. 2007. Discussion tracking in Enron email using PARAFAC. In Survey of Text Mining: Clustering Classification and Retrieval 2nd Ed. M. W. Berry and M. Castellanos Eds. 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Hierarchical structure and the prediction of missing links in networks. Nature 453. Clauset A. Moore C. and Newman M. 2008. Hierarchical structure and the prediction of missing links in networks. Nature 453 .","DOI":"10.1038\/nature06830"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/57167.57214"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2006.03.005"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1117454.1117456"},{"key":"e_1_2_1_15_1","first-page":"1","article-title":"Foundations of the PARAFAC procedure: Models and conditions for an \u201cexplanatory\u201d multi-modal factor analysis","volume":"16","author":"Harshman R. A.","year":"1970","unstructured":"Harshman , R. A. 1970 . Foundations of the PARAFAC procedure: Models and conditions for an \u201cexplanatory\u201d multi-modal factor analysis . 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