This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...

Topology detection (TD) in the context of power distribution networks (PDNs) is a fundamental requirement for a wide range of applications, such as fault localization and load management. PDNs suffer ...

Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks. By incorporating the graph topology into the neural network architecture through ...

Official implementation of the paper "Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations", TMLR, April 2024. Authors: Elia Cunegatti, Matteo Farina, Doina ...

Learning robust representations for nodes in graphs is crucial for graph learning tasks. Graph Neural Networks(GNNs) attract much attention recently as the frameworks achieve great success in node ...