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Has large-scale named-entity network analysis been resting on a flawed assumption?

Fegley BD, Torvik VI - PLoS ONE (2013)

Bottom Line: In both cases, we find that splitting has relatively little effect, whereas lumping has a dramatic effect on network measures.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.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America.

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.

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Cumulative distributions of collaborator counts (degree) for PubMed (2003–2007) and USPTO (2003–2007).Note that in both cases, the disambiguated data exhibits much more curvature than for the name = identity assumption.
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pone-0070299-g008: Cumulative distributions of collaborator counts (degree) for PubMed (2003–2007) and USPTO (2003–2007).Note that in both cases, the disambiguated data exhibits much more curvature than for the name = identity assumption.

Mentions: Collaboration networks such as ones constructed from co-authorships in PubMed and co-inventorships in USPTO are often characterized as scale-free networks. Scale-free networks exhibit a power law degree distribution whereby most vertices have few edges and a small minority of vertices have many edges (making such vertices hubs). When plotted on log-log scale, an empirical degree distribution follows a straight line when it follows a pure power law. While a straight-line fit on a log-log plot is not sufficient evidence of a power law; it is necessary [58], [60], [61]. Both Table 5 and Figure 8 show explicitly that the networks constructed from PubMed and USPTO do not follow a pure power law, with or without disambiguation; rather, the power law is limited to a certain range. Much of the range of the power law disappears with disambiguation. Figure 8 shows that the empirical distributions have at least three components: an initial “hook” (or curve); a line; and a curved tail (or cutoff). (The mixture model in Figure 9 shows this too. Note [6], [62].) The initial hook in the curves probably reflects the fact that most authors represented in the PubMed dataset obtain three to four co-authors in a single instance. Inventors behave similarly; but the hook is narrower, because their norm favors sole or dual attribution. The curved tail probably reflects the finite limits of human capacity (physical and mental). For a moderate number of collaborators, we see a linear trend in the non-disambiguated set, whereas the disambiguated set exhibits some curvature throughout the moderate range (as shown by the narrow range of the linear fit in Figure 8). In other words, the apparent power-law fit becomes an effect of ambiguity, because disambiguation reduces the linear piece of the distribution.


Has large-scale named-entity network analysis been resting on a flawed assumption?

Fegley BD, Torvik VI - PLoS ONE (2013)

Cumulative distributions of collaborator counts (degree) for PubMed (2003–2007) and USPTO (2003–2007).Note that in both cases, the disambiguated data exhibits much more curvature than for the name = identity assumption.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3722140&req=5

pone-0070299-g008: Cumulative distributions of collaborator counts (degree) for PubMed (2003–2007) and USPTO (2003–2007).Note that in both cases, the disambiguated data exhibits much more curvature than for the name = identity assumption.
Mentions: Collaboration networks such as ones constructed from co-authorships in PubMed and co-inventorships in USPTO are often characterized as scale-free networks. Scale-free networks exhibit a power law degree distribution whereby most vertices have few edges and a small minority of vertices have many edges (making such vertices hubs). When plotted on log-log scale, an empirical degree distribution follows a straight line when it follows a pure power law. While a straight-line fit on a log-log plot is not sufficient evidence of a power law; it is necessary [58], [60], [61]. Both Table 5 and Figure 8 show explicitly that the networks constructed from PubMed and USPTO do not follow a pure power law, with or without disambiguation; rather, the power law is limited to a certain range. Much of the range of the power law disappears with disambiguation. Figure 8 shows that the empirical distributions have at least three components: an initial “hook” (or curve); a line; and a curved tail (or cutoff). (The mixture model in Figure 9 shows this too. Note [6], [62].) The initial hook in the curves probably reflects the fact that most authors represented in the PubMed dataset obtain three to four co-authors in a single instance. Inventors behave similarly; but the hook is narrower, because their norm favors sole or dual attribution. The curved tail probably reflects the finite limits of human capacity (physical and mental). For a moderate number of collaborators, we see a linear trend in the non-disambiguated set, whereas the disambiguated set exhibits some curvature throughout the moderate range (as shown by the narrow range of the linear fit in Figure 8). In other words, the apparent power-law fit becomes an effect of ambiguity, because disambiguation reduces the linear piece of the distribution.

Bottom Line: In both cases, we find that splitting has relatively little effect, whereas lumping has a dramatic effect on network measures.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.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America.

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.

Show MeSH
Related in: MedlinePlus