Graph neural networks (GNNs) compose layers of graph filters and point-wise non-linearities

In this comprehensive review, we embark on a journey through the multifaceted land-scape of Graph Neural Networks, encompassing an array of critical aspects. Our study is motivated by …

CNNs and MLPs are specifically designed to handle non-Euclidean data, such as graphs and hyperbolic spaces, without any modifications.

What are the fundamental motivations and mechanics that drive Graph Neural Networks, what are the diferent variants, and what are their applications?

It categorize graph neural networks (GNNs) into recurrent graph neural networks (RecGNNs), convolutional graph neural networks (ConvGNNs), graph autoencoders (GAEs), and spatial- …

Graph Neural Networks Shuiwang Ji, Xiner Li, Shurui Gui Department of Computer Science & Engineering Texas A&M University These slides are based on Chapter 13 of Deep Learning: …

In this chapter, we turn our focus to more complex encoder models. We will introduce the graph neural network (GNN) formalism, which is a general framework for defining deep neural …