  The epistemic position of an agent often depends on their position in a
larger network of other agents who provide them with information. In general,
agents are better off if they have diverse and independent sources. Sullivan et
al. [2020] developed a method for quantitatively characterizing the epistemic
position of individuals in a network that takes into account both diversity and
independence; and presented a proof-of-concept, closed-source implementation on
a small graph derived from Twitter data [Sullivan et al. 2020]. This paper
reports on an open-source re-implementation of their algorithm in Python,
optimized to be usable on much larger networks. In addition to the algorithm
and package, we also show the ability to scale up our package to large
synthetic social network graph profiling, and finally demonstrate its utility
in analyzing real-world empirical evidence of `echo chambers' on online social
media, as well as evidence of interdisciplinary diversity in an academic
communications network.
