Topic: Graph Neural Networks

Develop a framework for network tomography combining graph neural networks with large language models. The GENIE ANR project investigates generalization across unseen network topologies, inference of non-additive metrics, and Graph-RAG diagnostics grounded in incident records and routing information. The doctorate includes experimental validation on realistic network data and peer-reviewed dissemination, within the ERMINE team in collaboration with ADOPNET. Appointment: 3 years; advertised start 2027-01-04. monthly gross salary 2300 euros.

Yixuan He (Arizona State University) considers graph neural networks for social, financial and biological networks in which edge signs and directions contain information needed for clustering and prediction. After an introduction to graph neural networks, the talk explains how signed and directed methods preserve relationships lost when every connection is treated as unsigned and undirected. The seminar then asks when a representation learned before the final task can transfer to a different graph or to a setting with few labels. Two theoretical perspectives address transfer across graph doma

Alex Wiltschko explains efforts to predict smell from molecular structure and to give computers a useful representation of odors. He discusses graph neural networks, the difficulty of collecting olfactory data, and how learned embeddings connect chemistry to perception and potential practical applications.

One of the important subfields of computational neuroscience is network neuroscience, which widely uses machine learning techniques, these include artificial neural networks. The latter ones owe a lot to neuroscience in the history of their development. Artificial neural networks (ANN) are often represented in the form of a graph – a visual representation of connections between the elements of the system, which form a network. However, this does not mean that an ANN was designed based on graph theory or with consideration of the special properties of complex networks. These representations ar

Schizophrenia is considered to be a complex illness with multiple pathways contributing to its clinical phenotype and is argued to be a disorder of disrupted brain network organization. Structural covariance in schizophrenia indicates a system-level brain maturation disruption overlapping with patterns of disturbed functional connectivity. Structural covariance networks are typically computed at the group-level, which prevents individualized inferences. To advance precision medicine of schizophrenia, a reliable mapping of brain system-level heterogeneity at the subject-level is critical. Here,

Mental simulation—the capacity to imagine what will or what could be—is a salient feature of human cognition, playing a key role in a wide range of cognitive abilities. In artificial intelligence, the last few years have seen the development of methods which are analogous to mental models and mental simulation. In this talk, I will discuss recent methods in deep learning for constructing such models from data and learning to use them via reinforcement learning, and compare such approaches to human mental simulation. While a number of challenges remain in matching the capacity of human mental s