Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Journal of the American Society for Information Science and Technology
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Also by the same author: AKTiveAuthor, a citation graph approach to name disambiguation
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Collective entity resolution in relational data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Efficient topic-based unsupervised name disambiguation
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
On co-authorship for author disambiguation
Information Processing and Management: an International Journal
Effective self-training author name disambiguation in scholarly digital libraries
Proceedings of the 10th annual joint conference on Digital libraries
Annual Review of Information Science and Technology
Efficient name disambiguation for large-scale databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Mapping scientific communities to scale-up ethnographies
Proceedings of the 2012 iConference
A tool for generating synthetic authorship records for evaluating author name disambiguation methods
Information Sciences: an International Journal
Computer assisted extraction, merging and correlation of identities with tracks inspector
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
| Hi-index | 0.00 |
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, that is those meso-level structures of a network resulting from groupings of nodes and their interlinking. 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 the commonality of last names, 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.