{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:19:28Z","timestamp":1765959568860,"version":"build-2065373602"},"reference-count":15,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2010,11,18]],"date-time":"2010-11-18T00:00:00Z","timestamp":1290038400000},"content-version":"vor","delay-in-days":686,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc of Assoc for Info"],"published-print":{"date-parts":[[2009,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper we outline a heuristic algorithm for disambiguating author names of publications via deterministic clustering based on well\u2010defined similarity measures between publications in which their names appear as authors. The algorithm is designed to be used in the construction of a collaboration network, i.e., a graph of author nodes and co\u2010author links. In this context, the goal is to produce a co\u2010authorship graph with network characteristics that are close to those of the \u201ctrue\u201d collaboration network, so that meaningful network metrics can be determined.<\/jats:p><jats:p>The algorithm we present here is fairly easily comprehended as it does not depend on any sophisticated AI techniques. This is important in the context of policy studies, in which we successfully applied it, as it enables policy makers to judge the soundness of the methodology with considerable confidence. It is also quite fast, making it possible to run large\u2010scale analyses (here, in the order of a hundred thousand publications and in the order of a million names to be disambiguated) on a moderately sized desktop computer within a few days.<\/jats:p><jats:p>The algorithm is, finally, open to improvement via extensions that take into account additional kinds of fields in bibliographic records of publications to provide evidence that two occurrences of similar names belong to the same individual.<\/jats:p>","DOI":"10.1002\/meet.2009.1450460218","type":"journal-article","created":{"date-parts":[[2010,1,29]],"date-time":"2010-01-29T10:31:56Z","timestamp":1264761116000},"page":"1-20","source":"Crossref","is-referenced-by-count":18,"title":["Author name disambiguation for collaboration network analysis and visualization"],"prefix":"10.1002","volume":"46","author":[{"given":"Andreas","family":"Strotmann","sequence":"first","affiliation":[]},{"given":"Dangzhi","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Tania","family":"Bubela","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2010,11,18]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"Andrade M.& al. (2006) Workshop on scholarly databases and data integration. Bloomington Indiana. Retrieved August 8 2008 fromhttp:\/\/scimaps.org\/meeting_060830.php."},{"key":"e_1_2_7_3_1","unstructured":"Bubela T.&Strotmann A.(2008).Designing metrics to assess the impacts and social benefits of publicly funded research in health and agricultural biotechnology. Case study The International Expert Group on Biotechnology Innovation and Intellectual Property. Retrieved January 26 2009 fromhttp:\/\/www.theinnovationpartnership.org\/data\/ieg\/documents\/cases\/TIP_Innovation_Metrics_Case_Study.pdf"},{"key":"e_1_2_7_4_1","unstructured":"Chen C. M.(2007).CiteSpace: visualizing patterns and trends in scientific literature. Retrieved November 6 2007 fromhttp:\/\/cluster.cis.drexel.edu\/\u2010cchen\/citespace\/"},{"key":"e_1_2_7_5_1","doi-asserted-by":"crossref","unstructured":"Han J.Zha H. Y.&Giles C. L.(2005).Name Disambiguation in Author Citations using a K\u2010way Spectral Clustering Method. 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To appear in 12th International Conference of the International Society for Scientometrics and Informetrics 2009 Rio de Janeiro Brazil"},{"key":"e_1_2_7_12_1","unstructured":"Thomson Reuters Science(2009).Distinct Author Identification System. 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