47 citations · 115 across the 30 of their papers we have counts for
32 papers
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…
Explainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms
Ian Davidson, Michael Livanos, Antoine Gourru +3
Explainable AI (XAI) is an important developing area but remains relatively understudied for clustering. We propose an explainable-by-design clustering approach that not only finds…
Interactions in Information Spread
Gaël Poux-Médard
Since the development of writing 5000 years ago, human-generated data gets produced at an ever-increasing pace. Classical archival methods aimed at easing information retrieval. No…
Properties of Reddit News Topical Interactions
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
Most models of information diffusion online rely on the assumption that pieces of information spread independently from each other. However, several works pointed out the necessity…
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…
Interactions in Information Spread
Gaël Poux-Médard, Julien Velcin, Sabine Loudcher
Large quantities of data flow on the internet. When a user decides to help the spread of a piece of information (by retweeting, liking, posting content), most research works assume…
Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes
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…
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics
Clément Christophe, Julien Velcin, Jairo Cugliari +2
Slow emerging topic detection is a task between event detection, where we aggregate behaviors of different words on short period of time, and language evolution, where we monitor t…
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…
New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding
Robin Brochier, Antoine Gourru, Adrien Guille +1
The scientific literature is growing faster than ever. Finding an expert in a particular scientific domain has never been as hard as today because of the increasing amount of publi…
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…
Document Network Projection in Pretrained Word Embedding Space
Antoine Gourru, Adrien Guille, Julien Velcin +1
We present Regularized Linear Embedding (RLE), a novel method that projects a collection of linked documents (e.g. citation network) into a pretrained word embedding space. In addi…
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…
Link Prediction with Mutual Attention for Text-Attributed Networks
Robin Brochier, Adrien Guille, Julien Velcin
In this extended abstract, we present an algorithm that learns a similarity measure between documents from the network topology of a structured corpus. We leverage the Scaled Dot-P…
Global Vectors for Node Representations
Robin Brochier, Adrien Guille, Julien Velcin
Most network embedding algorithms consist in measuring co-occurrences of nodes via random walks then learning the embeddings using Skip-Gram with Negative Sampling. While it has pr…
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…
Peerus Review: a tool for scientific experts finding
Robin Brochier, Adrien Guille, Julien Velcin +2
We propose a tool for experts finding applied to academic data generated by the start-up DSRT in the context of its application Peerus. A user may submit the title, the abstract an…
Impact of the Query Set on the Evaluation of Expert Finding Systems
Robin Brochier, Adrien Guille, Benjamin Rothan +1
Expertise is a loosely defined concept that is hard to formalize. Much research has focused on designing efficient algorithms for expert finding in large databases in various appli…
Automatic Language Identification for Romance Languages using Stop Words and Diacritics
Ciprian-Octavian Truică, Julien Velcin, Alexandru Boicea
Automatic language identification is a natural language processing problem that tries to determine the natural language of a given content. In this paper we present a statistical m…