186 citations · 205 across the 15 of their papers we have counts for
18 papers
A Grammar of French Civil Cassation: A Ground-Level Dataset and Tool
Avrile Floro, Tamara Dhorasoo, Nils Holzenberger +3
This paper presents a dataset of 121,536 ground-reasons pairs across 86,464 rulings of the five civil chambers of the French Court of Cassation (2016-2025). An appeal on points of…
k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoods
Lilian Marey, Tiphaine Viard, Charlotte Laclau
Link prediction (LP) plays a central role in graph-based applications, particularly in social recommendation. However, real-world graphs often reflect structural biases, most notab…
Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction
Lilian Marey, Mathilde Perez, Tiphaine Viard +1
Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this…
Modeling Musical Genre Trajectories through Pathlet Learning
Lilian Marey, Charlotte Laclau, Bruno Sguerra +2
The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user prefe…
Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI
Timothée Schmude, Mireia Yurrita, Kars Alfrink +3
Explainability and its emerging counterpart contestability have become important normative and design principles for trustworthy AI as they enable users and subjects to understand…
Graph as a feature: improving node classification with non-neural graph-aware logistic regression
Simon Delarue, Thomas Bonald, Tiphaine Viard
Graph Neural Networks (GNNs) and their message passing framework that leverages both structural and feature information, have become a standard method for solving graph-based machi…
Evaluating graph-based explanations for AI-based recommender systems
Simon Delarue, Astrid Bertrand, Tiphaine Viard
Recent years have witnessed a rapid growth of recommender systems, providing suggestions in numerous applications with potentially high social impact, such as health or justice. Me…
Exploring and mining attributed sequences of interactions
Tiphaine Viard, Henry Soldano, Guillaume Santini
We are faced with data comprised of entities interacting over time: this can be individuals meeting, customers buying products, machines exchanging packets on the IP network, among…
Classifying Wikipedia in a fine-grained hierarchy: what graphs can contribute
Tiphaine Viard, Thomas McLachlan, Hamidreza Ghader +1
Wikipedia is a huge opportunity for machine learning, being the largest semi-structured base of knowledge available. Because of this, many works examine its contents, and focus on…
Introducing multilayer stream graphs and layer centralities
Pimprenelle Parmentier, Tiphaine Viard, Benjamin Renoust +1
Graphs are commonly used in mathematics to represent some relationships between items. However, as simple objects, they sometimes fail to capture all relevant aspects of real-world…
Degree-based Outlier Detection within IP Traffic Modelled as a Link Stream
Audrey Wilmet, Tiphaine Viard, Matthieu Latapy +1
This paper aims at precisely detecting and identifying anomalous events in IP traffic. To this end, we adopt the link stream formalism which properly captures temporal and structur…
Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks
Matthieu Latapy, Clémence Magnien, Tiphaine Viard
We recently introduced a formalism for the modeling of temporal networks, that we call stream graphs. It emphasizes the streaming nature of data and allows rigorous definitions of…
Movie rating prediction using content-based and link stream features
Tiphaine Viard, Raphaël Fournier-S'niehotta
While graph-based collaborative filtering recommender systems have been introduced several years ago, there are still several shortcomings to deal with, the temporal information be…
Enumerating maximal cliques in link streams with durations
Tiphaine Viard, Clémence Magnien, Matthieu Latapy
Link streams model interactions over time, and a clique in a link stream is defined as a set of nodes and a time interval such that all pairs of nodes in this set interact permanen…
Stream Graphs and Link Streams for the Modeling of Interactions over Time
Matthieu Latapy, Tiphaine Viard, Clémence Magnien
Graph theory provides a language for studying the structure of relations, and it is often used to study interactions over time too. However, it poorly captures the both temporal an…
Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams
Tiphaine Viard, Raphaël Fournier-S'niehotta, Clémence Magnien +1
Studying IP traffic is crucial for many applications. We focus here on the detection of (structurally and temporally) dense sequences of interactions, that may indicate botnets or…
Analysis of the temporal and structural features of threads in a mailing-list
Noé Gaumont, Tiphaine Viard, Raphaël Fournier-S'niehotta +2
A link stream is a collection of triplets indicating that an interaction occurred between and at time . Link streams model many real-world situations like emai…
Computing maximal cliques in link streams
Tiphaine Viard, Matthieu Latapy, Clémence Magnien
A link stream is a collection of triplets indicating that an interaction occurred between u and v at time t. We generalize the classical notion of cliques in graphs to…