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Ruling out static latent homophily in citation networks

Overview of attention for article published in Scientometrics, December 2016
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Title
Ruling out static latent homophily in citation networks
Published in
Scientometrics, December 2016
DOI 10.1007/s11192-016-2194-9
Pubmed ID
Authors

Peter Wittek, Sándor Darányi, Gustaf Nelhans

Abstract

Citation and coauthor networks offer an insight into the dynamics of scientific progress. We can also view them as representations of a causal structure, a logical process captured in a graph. From a causal perspective, we can ask questions such as whether authors form groups primarily due to their prior shared interest, or if their favourite topics are 'contagious' and spread through co-authorship. Such networks have been widely studied by the artificial intelligence community, and recently a connection has been made to nonlocal correlations produced by entangled particles in quantum physics-the impact of latent hidden variables can be analyzed by the same algebraic geometric methodology that relies on a sequence of semidefinite programming (SDP) relaxations. Following this trail, we treat our sample coauthor network as a causal graph and, using SDP relaxations, rule out latent homophily as a manifestation of prior shared interest only, leading to the observed patternedness. By introducing algebraic geometry to citation studies, we add a new tool to existing methods for the analysis of content-related social influences.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Russia 1 4%
Unknown 22 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 21%
Librarian 4 17%
Student > Bachelor 4 17%
Other 3 13%
Researcher 3 13%
Other 4 17%
Unknown 1 4%
Readers by discipline Count As %
Social Sciences 10 42%
Engineering 4 17%
Arts and Humanities 2 8%
Computer Science 2 8%
Medicine and Dentistry 2 8%
Other 3 13%
Unknown 1 4%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 December 2016.
All research outputs
#18,487,595
of 22,908,162 outputs
Outputs from Scientometrics
#2,281
of 2,688 outputs
Outputs of similar age
#304,600
of 416,052 outputs
Outputs of similar age from Scientometrics
#45
of 56 outputs
Altmetric has tracked 22,908,162 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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