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Properly identifying the author of a scientific article is an important task for giving credit, tracking progress, and identifying ideas’ lineages. Usually, publications and citations do not provide unique identifiers to authors but only the raw string character representation of their name and affiliation. The fundamental problem is that an author might change the string representations due to changing in name spelling (e.g., removing accents), journal limitations (e.g., only allow first letter of first name), or simply two people having the same name. Several researchers have proposed methods to solve this problem, but most methods do not scale well and are not open to the community. In this work, we develop a scalable method that we make publicly available to disambiguate large-scale publications.
Tong Zeng was funded by the China Scholarship Council #201706190067. Daniel E. Acuna was funded by the National Science Foundation awards #1646763 and #1800956.
Author name disambiguation, Entity resolution, Approximate nearest neighbor, Minimum-spanning tree, Clustering
Author name disambiguation, Entity resolution, Approximate nearest neighbor, Minimum-spanning tree, Clustering
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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