Search Results for author:
Found 11 papers, 5 papers with code
Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks
On the other hand, a new family of Mixed Membership Stochastic Block Models (MMSBM) allows to model static labeled networks under the assumption of mixed-membership clustering.
Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks
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.
Multivariate Powered Dirichlet Hawkes Process
Finally, we develop a use case of the MPDHP on Reddit data.
Interactions in Information Spread
The amount of data that gets generated daily is beyond human comprehension, and appeals for new information retrieval strategies.
Properties of Reddit News Topical Interactions
Most models of information diffusion online rely on the assumption that pieces of information spread independently from each other.
Serialized Interacting Mixed Membership Stochastic Block Model
In this work, we show that these models are all special cases of a single global framework: the Serialized Interacting Mixed membership Stochastic Block Model (SIMSBM).
Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes
PDHP also alleviates the hypothesis that textual content and temporal dynamics are perfectly correlated.
Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior
Furthermore, the textual content of a document is not always linked to its temporal dynamics.
Information Interaction Profile of Choice Adoption
We introduce an efficient method to infer both the entities interaction network and its evolution according to the temporal distance separating interacting entities; together, they form the interaction profile.
Powered Dirichlet Process for Controlling the Importance of "Rich-Get-Richer" Prior Assumptions in Bayesian Clustering
This process allows nonparametric estimation of the number of clusters when partitioning datasets.
Interactions in information spread: quantification and interpretation using stochastic block models
Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.)
