Xiaoran Yan

TL;DR: A statistical model is developed that quantifies the effect of the majority illusion and shows that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

TL;DR: The first principled and tractable approach to model selection between standard and degree-corrected block models is presented, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs.

TL;DR: In this paper, the authors introduce a stochastic model for network communication that combines local and global information about the network topology to generate biased random walks on the network and investigate the effects of varying the global information bias on the communication cost.

TL;DR: This work adopts a generative modeling approach called weighted stochastic block models (WSBM) that can describe a wider range of community structure topologies by explicitly considering patterned interactions between communities in brain networks that go beyond modularity.

TL;DR: This paper combines classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community, and has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm.