  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.
