Graph Neural Networks

Brain-Grasp: Graph-Based Saliency Priors for Improved FMRI-Based Visual Brain Decoding

Beyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning

Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of …

Saliency Matters: From Nodes to Objects

Due to their intrinsic capabilities in capturing, modeling, and representing relationships between pieces of information, graphs have been used in a wide variety of application …

wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction

Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose …