  Information spread on networks can be efficiently modeled by considering
three features: documents' content, time of publication relative to other
publications, and position of the spreader in the network. Most previous works
model up to two of those jointly, or rely on heavily parametric approaches.
Building on recent Dirichlet-Point processes literature, we introduce the
Houston (Hidden Online User-Topic Network) model, that jointly considers all
those features in a non-parametric unsupervised framework. It infers dynamic
topic-dependent underlying diffusion networks in a continuous-time setting
along with said topics. It is unsupervised; it considers an unlabeled stream of
triplets shaped as \textit{(time of publication, information's content,
spreading entity)} as input data. Online inference is conducted using a
sequential Monte-Carlo algorithm that scales linearly with the size of the
dataset. Our approach yields consequent improvements over existing baselines on
both cluster recovery and subnetworks inference tasks.
