Graph Neural Networks
GNNs are a type of neural network designed to work with data that can be represented as graphs. They excel at capturing relationships between nodes (vertices) and their connections (edges). GNNs aggregate information from a node's neighbors to learn and predict properties or classifications based on the graph's structure. They’re useful in applications like social network analysis, recommendation systems, and molecular chemistry.
Related Conference of Graph Neural Networks
March 09-10, 2026
14th Global Summit on Artificial Intelligence and Neural Networks
Singapore City, Singapore
December 07-08, 2026
12th International Conference and Exhibition on Mechanical & Aerospace Engineering
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December 09-10, 2026
25th International Conference on Big Data & Data Analytics
Amsterdam, Netherlands
Graph Neural Networks Conference Speakers
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- Deep Learning
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