Node-level tasks are a primary focus of Graph Neural Networks (GNNs) and involve predicting attributes or categories for individual nodes within a graph. These tasks utilize the graph structure to leverage both…

Graph-based models, particularly Graph Neural Networks (GNNs), have been widely used in various supervised learning tasks that involve graph-structured data. These tasks leverage the unique ability of GNNs to learn from both…

Node and graph classification are two fundamental tasks in the application of Graph Neural Networks (GNNs). These tasks involve predicting labels for either individual nodes within a graph (node classification) or entire…

Heterogeneous graphs, also known as heterogeneous information networks, contain multiple types of nodes and edges, representing different entities and relationships. These graphs are more complex than homogeneous graphs, where all nodes and…

Multi-head attention is a powerful technique used in Graph Neural Networks (GNNs), particularly in models like Graph Attention Networks (GATs), to enhance their learning capabilities. Inspired by its success in natural language…

As Graph Neural Networks (GNNs) are applied to deeper architectures, they often encounter a problem known as over-smoothing. Over-smoothing is a phenomenon where, after several layers of message passing, the node representations…

Graph Attention Networks (GATs) are an advanced type of Graph Neural Network (GNN) that incorporate attention mechanisms to improve the aggregation of node features. Traditional GNNs aggregate features from a node's neighbors…

Cluster-GCN is an innovative approach designed to scale Graph Neural Networks (GNNs) to large graphs by using a method called graph-wise sampling. This approach addresses the computational and memory challenges associated with…

Fast Graph Convolutional Networks (FastGCN) is an advanced approach designed to address the scalability issues inherent in traditional Graph Convolutional Networks (GCNs). As graphs grow larger, the computational and memory requirements for…

Graph Sample and Aggregate (GraphSAGE) is a popular framework designed to improve the scalability and performance of Graph Neural Networks (GNNs) on large graphs. Unlike traditional Graph Convolutional Networks (GCNs) that require…