{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:49:17Z","timestamp":1761562157176,"version":"3.30.1"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2016,8,1]],"date-time":"2016-08-01T00:00:00Z","timestamp":1470009600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec id=\"j_jdis.201620_s_006_w2aab2b8c62b1b7b1aab1c15b1Aa\">\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p> This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_jdis.201620_s_007_w2aab2b8c62b1b7b1aab1c15b2Aa\">\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>We compare three types of networks: unweighted networks, in which a link represents a past collaboration; weighted networks, in which links are weighted by the number of joint publications; and bipartite author-publication networks. The analysis investigates their relation to positive stability, as well as their potential in predicting links in future versions of the co-authorship network. Several hypotheses are tested.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_jdis.201620_s_008_w2aab2b8c62b1b7b1aab1c15b3Aa\">\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p> Among other results, we find that weighted networks do not automatically lead to better predictions. Bipartite networks, however, outperform unweighted networks in almost all cases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_jdis.201620_s_009_w2aab2b8c62b1b7b1aab1c15b4Aa\">\n                  <jats:title>Research limitations<\/jats:title>\n                  <jats:p> Only two relatively small case studies are considered.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_jdis.201620_s_010_w2aab2b8c62b1b7b1aab1c15b5Aa\">\n                  <jats:title>Practical implications<\/jats:title>\n                  <jats:p> The study suggests that future link prediction studies on co-occurrence networks should consider using the bipartite network as a training network.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_jdis.201620_s_011_w2aab2b8c62b1b7b1aab1c15b6Aa\">\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p> This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.<\/jats:p>\n               <\/jats:sec>","DOI":"10.20309\/jdis.201620","type":"journal-article","created":{"date-parts":[[2016,10,12]],"date-time":"2016-10-12T08:28:12Z","timestamp":1476260892000},"page":"59-78","source":"Crossref","is-referenced-by-count":1,"title":["Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases"],"prefix":"10.2478","volume":"1","author":[{"given":"Raf","family":"Guns","sequence":"first","affiliation":[{"name":"Centre for R&D Monitoring (ECOOM) , University of Antwerp , Antwerp 2020 , Belgium"}]}],"member":"374","published-online":{"date-parts":[[2017,9,1]]},"reference":[{"key":"2024120712084524486_j_jdis.201620_ref_001_w2aab2b8c62b1b7b1ab2b1b1Aa","doi-asserted-by":"crossref","unstructured":"Barab\u00e1si, A.L., & Albert, R. 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