GNN Meaning and
Definition
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A GNN, or Graph Neural Network, refers to a machine learning model that is specifically designed to process and analyze structured data in the form of graphs. Graphs are mathematical abstractions that consist of nodes or vertices connected by edges. These nodes can represent entities or concepts, and the edges represent the relationships or connections between these entities. GNNs are powerful tools used in various fields, including social network analysis, bioinformatics, recommendation systems, and computer vision.
The key characteristic of GNNs is their ability to capture both the local neighborhood information and the global structural information of the graph. This is achieved through an iterative process where each node in the graph collaboratively aggregates information from its neighboring nodes. By combining information from different nodes and layers, the GNN is able to generate complex representations that encode both local and global patterns.
GNNs often employ a deep learning architecture, consisting of multiple layers of graph convolutions or message-passing operations. These layers allow the GNN to gradually learn and refine the node representations, incorporating information from different parts of the graph. The final node representations can then be used for various downstream tasks, such as node classification, link prediction, or graph generation.
Overall, GNNs offer a powerful framework for dealing with structured data in the form of graphs. They enable the learning of rich representations that capture both local and global patterns, making them valuable in a wide range of applications.
Common Misspellings for GNN
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