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Related Experiment Video

Updated: May 9, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

Brent D Fegley¹, Vetle I Torvik

¹Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America.

|July 30, 2013

Summary

Name ambiguity in scientific networks causes errors. Lumping, or merging distinct entities due to shared names, dramatically distorts network measures, while splitting has minimal impact.

Related Experiment Videos

Last Updated: May 9, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Bibliometrics
Network Science
Information Science

Background:

Named-entity recognition often assumes unique identification, leading to splitting (one entity as many) and lumping (many entities as one) errors.
These errors can significantly impact the analysis of large-scale networks, particularly in collaborative fields like science and technology.

Purpose of the Study:

To quantify the effects of name ambiguity errors (splitting and lumping) on network measures.
To compare the impact of these errors on co-author networks in biomedicine and co-inventor networks in U.S. patents.

Main Methods:

Analysis of disambiguated bibliographic datasets from PubMed (co-authorship) and USPTO (co-inventor) covering 2003-2007.
Evaluation of common network measures, including global clustering coefficient, degree assortativity, and average shortest path length, under conditions of splitting and lumping.

Main Results:

Splitting errors had a minor effect on network measures.
Lumping errors dramatically altered network measures, reducing clustering coefficients and assortativity, and shortening average path lengths.
Lumping artificially inflated vertex degrees and created spurious intransitive relationships.

Conclusions:

Name ambiguity, particularly lumping, is a critical artifact affecting bibliometric network analysis.
The observed power-law distribution in collaborator counts may be partly an artifact of name ambiguity.
Disambiguation strategies are crucial for accurate network analysis in scientific and technological contexts.