title: Has large-scale named-entity network analysis been resting on a flawed assumption?
abstract: The assumption that a name uniquely identifies an entity introduces two types of errors: splitting treats one entity as two or more (because of name variants); lumping treats multiple entities as if they were one (because of shared names). Here we investigate the extent to which splitting and lumping affect commonly-used measures of large-scale named-entity networks within two disambiguated bibliographic datasets: one for co-author names in biomedicine (PubMed, 2003-2007); the other for co-inventor names in U.S. patents (USPTO, 2003-2007). In both cases, we find that splitting has relatively little effect, whereas lumping has a dramatic effect on network measures. For example, in the biomedical co-authorship network, lumping (based on last name and both initials) drives several measures down: the global clustering coefficient by a factor of 4 (from 0.265 to 0.066); degree assortativity by a factor of ∼13 (from 0.763 to 0.06); and average shortest path by a factor of 1.3 (from 5.9 to 4.5). These results can be explained in part by the fact that lumping artificially creates many intransitive relationships and high-degree vertices. This effect of lumping is much less dramatic but persists with measures that give less weight to high-degree vertices, such as the mean local clustering coefficient and log-based degree assortativity. Furthermore, the log-log distribution of collaborator counts follows a much straighter line (power law) with splitting and lumping errors than without, particularly at the low and the high counts. This suggests that part of the power law often observed for collaborator counts in science and technology reflects an artifact: name ambiguity.
authors.affiliation: Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America.
authors.forename: Brent D
authors.initials: BD
authors.lastname: Fegley
authors.forename: Vetle I
authors.initials: VI
authors.lastname: Torvik
mesh_terms: D000465:Algorithms; D009275:Names; D011642:Publications
references.citation: Cognition. 2012 Mar;122(3):280-91
references.pmid: 22192697
references.citation: Proc Natl Acad Sci U S A. 2004 Apr 6;101 Suppl 1:5200-5
references.pmid: 14745042
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Feb;87(2):022801
references.pmid: 23496562
references.citation: Proc Natl Acad Sci U S A. 2002 Jun 11;99(12):7821-6
references.pmid: 12060727
references.citation: Nature. 2000 Jul 27;406(6794):378-82
references.pmid: 10935628
references.citation: Nature. 2008 Feb 14;451(7180):766-7
references.pmid: 18272993
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Sep;82(3 Pt 2):037102
references.pmid: 21230212
references.citation: Proc Natl Acad Sci U S A. 2001 Jan 16;98(2):404-9
references.pmid: 11149952
references.citation: Phys Rev Lett. 2002 Nov 11;89(20):208701
references.pmid: 12443515
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Dec;84(6 Pt 2):066117
references.pmid: 22304165
references.citation: ACM Trans Knowl Discov Data. 2009 Jul 1;3(3):
references.pmid: 20072710
references.citation: Theor Popul Biol. 2004 Jun;65(4):413-22
references.pmid: 15136015
references.citation: Science. 2005 Apr 29;308(5722):697-702
references.pmid: 15860629
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Sep;68(3 Pt 2):036122
references.pmid: 14524847
references.citation: AMIA Annu Symp Proc. 2003;:1033
references.pmid: 14728536
references.citation: Nature. 1998 Jun 4;393(6684):440-2
references.pmid: 9623998
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Mar;81(3 Pt 1):031135
references.pmid: 20365725
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2005 May;71(5 Pt 2):057101
references.pmid: 16089694