paper

Ruling Out Static Latent Homophily in Citation Networks

arXiv:1605.08185 · doi:10.1007/s11192-016-2194-9

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 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.

arXiv comment14 pages, 1 figure, computational details are available at https://github.com/peterwittek/ipython-notebooks/blob/master/Citation_Network_SDP.ipynb. in Scientometrics, 2016

References in corpus (8)