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

Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks