Jul 23, 2025 · Graph Convolutional Networks (GCNs) are a type of neural network designed to work directly with graphs. A graph consists of nodes (vertices) and edges (connections …

Aug 14, 2023 · Our Graph Convolutional Network (GCN) has effectively learned embeddings that group similar nodes into distinct clusters. This enables the final linear layer to distinguish them …

A convolutional neural network layer, in the context of computer vision, can be considered a GNN applied to graphs whose nodes are pixels and only adjacent pixels are connected by edges in …

Sep 2, 2021 · In this article, we will illustrate the challenges of computing over graphs, describe the origin and design of graph neural networks, and explore the most popular GNN variants in …

Graph Convolutional Networks (GCNs) extend the concept of convolutional networks to graph-structured data. The key idea is to perform convolution operations on graphs, which allows the …

That’s where Graph Convolutional Networks come in. They’re designed from the ground up to work with this kind of networked, relational data. And honestly, as more of our problems get …

This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, …

In this survey, we conduct a comprehensive review speci cally on the emerging eld of graph convolutional networks, which is one of the most prominent graph deep learning models.

Aug 1, 2025 · A graph convolutional network (GCN) is a neural network explicitly developed for processing graph-structured data input. Graph-structured data is represented as a graph, with …

In this paper, we study the problem of design-ing and analyzing deep graph convolutional net-works. We propose the GCNII, an extension of the vanilla GCN model with two simple yet ef …