16 citations · 18 across the 3 of their papers we have counts for
6 papers
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…
A Note on the Modeling Power of Different Graph Types
Josephine M. Thomas, Silvia Beddar-Wiesing, Alice Moallemy-Oureh +1
Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of g…
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space
Josephine Maria Thomas, Alessandro Muscoloni, Sara Ciucci +2
Complex network topologies and hyperbolic geometry seem specularly connected, and one of the most fascinating and challenging problems of recent complex network theory is to map a…