47 citations · 115 across the 30 of their papers we have counts for
13 papers · 1 filter
Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
Most real-world networks evolve over time. Existing literature proposes models for dynamic networks that are either unlabeled or assumed to have a single membership structure. On t…
Multivariate Powered Dirichlet Hawkes Process
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
The publication time of a document carries a relevant information about its semantic content. The Dirichlet-Hawkes process has been proposed to jointly model textual information an…
Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
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…
Serialized Interacting Mixed Membership Stochastic Block Model
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
Last years have seen a regain of interest for the use of stochastic block modeling (SBM) in recommender systems. These models are seen as a flexible alternative to tensor decomposi…
Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
The textual content of a document and its publication date are intertwined. For example, the publication of a news article on a topic is influenced by previous publications on simi…
Powered Dirichlet Process for Controlling the Importance of "Rich-Get-Richer" Prior Assumptions in Bayesian Clustering
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
One of the most used priors in Bayesian clustering is the Dirichlet prior. It can be expressed as a Chinese Restaurant Process. This process allows nonparametric estimation of the…
Information Interaction Profile of Choice Adoption
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
Interactions between pieces of information (entities) play a substantial role in the way an individual acts on them: adoption of a product, the spread of news, strategy choice, etc…
Interactions in information spread: quantification and interpretation using stochastic block models
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people…
Inductive Document Network Embedding with Topic-Word Attention
Robin Brochier, Adrien Guille, Julien Velcin
Document network embedding aims at learning representations for a structured text corpus i.e. when documents are linked to each other. Recent algorithms extend network embedding ap…
How to detect novelty in textual data streams? A comparative study of existing methods
Clément Christophe, Julien Velcin, Jairo Cugliari +2
Since datasets with annotation for novelty at the document and/or word level are not easily available, we present a simulation framework that allows us to create different textual…
Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models
Alberto Lumbreras, Julien Velcin, Marie Guégan +1
We present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density…
How to Use Temporal-Driven Constrained Clustering to Detect Typical Evolutions
Marian-Andrei Rizoiu, Julien Velcin, Stéphane Lallich
In this paper, we propose a new time-aware dissimilarity measure that takes into account the temporal dimension. Observations that are close in the description space, but distant i…
Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study
Md. Abul Hasnat, Julien Velcin, Stéphane Bonnevay +1
In this paper, we study different discrete data clustering methods, which use the Model-Based Clustering (MBC) framework with the Multinomial distribution. Our study comprises seve…