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We investigate how author name homonymy distorts clustered large-scaleco-author networks, and present a simple, effective, scalable and generalizablealgorithm to ameliorate such distortions. We evaluate the performance of thealgorithm to improve the resolution of mesoscopic network structures. To thisend, we establish the ground truth for a sample of author names that isstatistically representative of different types of nodes in the co-authornetwork, distinguished by their role for the connectivity of the network. Wefinally observe that this distinction of node roles based on the mesoscopicstructure of the network, in combination with a quantification of author namecommonality, suggests a new approach to assess network distortion by homonymyand to analyze the reduction of distortion in the network after disambiguation,without requiring ground truth sampling.

Resolving Author Name Homonymy to Improve Resolution of Structures in Co-author Networks