16 citations · 18 across the 4 of their papers we have counts for
5 papers · 1 filter
Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks
Silvia Beddar-Wiesing, Alice Moallemy-Oureh
Graph Neural Networks (GNNs) are known to match the distinguishing power of the 1-Weisfeiler-Lehman (1-WL) test, and the resulting partitions coincide with the unfolding tree equiv…
Absolute Evaluation Measures for Machine Learning: A Survey
Silvia Beddar-Wiesing, Alice Moallemy-Oureh, Marie Kempkes +1
Machine Learning is a diverse field applied across various domains such as computer science, social sciences, medicine, chemistry, and finance. This diversity results in varied eva…
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
Silvia Beddar-Wiesing, Giuseppe Alessio D'Inverno, Caterina Graziani +4
Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On…
Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network
Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Yannick Nagel +2
Temporal Point Processes (TPPs) have recently become increasingly interesting for learning dynamics in graph data. A reason for this is that learning on dynamic graph data is becom…
Graph Neural Networks Designed for Different Graph Types: A Survey
Josephine M. Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing +1
Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types wh…