  We investigate how author name homonymy distorts clustered large-scale
co-author networks, and present a simple, effective, scalable and generalizable
algorithm to ameliorate such distortions. We evaluate the performance of the
algorithm to improve the resolution of mesoscopic network structures. To this
end, we establish the ground truth for a sample of author names that is
statistically representative of different types of nodes in the co-author
network, distinguished by their role for the connectivity of the network. We
finally observe that this distinction of node roles based on the mesoscopic
structure of the network, in combination with a quantification of author name
commonality, suggests a new approach to assess network distortion by homonymy
and to analyze the reduction of distortion in the network after disambiguation,
without requiring ground truth sampling.
