New paper in Science Advances combining topic models and complex networks

Topic models are a popular way to extract information from text data, but its most popular flavours (based on Dirichlet priors, such as LDA) make unreasonable assumptions about the data which severely limit its applicability. Martin Gerlach, member of the Amaral-lab, and co-authors explore an alternative way of doing topic modelling, based on stochastic block models (SBM), thus exploiting a mathematical connection with finding community structure in networks.

A network approach to topic models
Science Advances 4, eaaq1360 (2018)

Code & more: TopSBM: Topic Models based on Stochastic Block Models