Model Architecture for Graph Classification
Graph classification is a critical task in Graph Neural Networks (GNNs) where the objective is to assign a label or category to an entire graph. The model architecture for graph classification involves…
Graph classification is a critical task in Graph Neural Networks (GNNs) where the objective is to assign a label or category to an entire graph. The model architecture for graph classification involves…
Learning from multiple graphs is a critical aspect of training Graph Neural Networks (GNNs) in scenarios where data is represented as a collection of small, separate graphs rather than a single large…
Graph classification is a fundamental task in the realm of Graph Neural Networks (GNNs) that involves predicting a label or category for an entire graph. The task is critical in various domains…
Graph-level tasks are advanced applications of Graph Neural Networks (GNNs) that focus on predicting labels, properties, or features for entire graphs rather than individual nodes or edges. These tasks include graph classification,…
Edge-level tasks are an important category of problems that Graph Neural Networks (GNNs) are designed to solve. These tasks involve predicting or inferring properties associated with edges in a graph, which represent…
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…