Bibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive maps
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Authorship identity has long been an Achilles’ heel in bibliometric analyses at the individual level. This problem appears in studies of scientists’ productivity, inventor mobility and scientific collaboration. Using the concepts of cognitive maps from psychology and approximate structural equivalence from network analysis, we develop a novel algorithm for name disambiguation based on knowledge homogeneity scores. We test it on two cases, and the results show that this approach outperforms other common authorship identification methods with the ASE method providing a relatively simple algorithm that yields higher levels of accuracy with reasonable time demands.
KeywordsName disambiguation Common names Cognitive map Approximate structural equivalence Knowledge homogeneity score Hierarchical clustering
The authors would like to thank Dr. Diana Hicks and participants in the Workshop for Original Policy Research at Georgia Tech, and the Research Institute of Economics, Technology and Industry, Tokyo, seminar series for their comments on earlier draft, Dr. Juan D. Rogers for offering the R-class and useful discussion, Dr. Philip Shapira for the use of the global nano publication data in the study, and the anonymous reviewer for his useful suggestions. We also would like to thank authors and co-authors of “Li, Y” for clarifying their publication lists.
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