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Graph Neural Networks
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Graph Neural Networks (GNNs) are a class of machine learning models specifically designed to handle data structured as graphs.
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In many real-world applications, data can be naturally represented as graphs. For example, social networks, citation networks, molecular structures, and knowledge graphs all exhibit graph-like structures. GNNs have gained significant attention and popularity due to their ability to model and analyze such data effectively.
The core idea behind GNNs is to learn representations for nodes in a graph by leveraging both the local features of individual nodes and the graph's global structure. GNNs achieve this through a process called message passing. In message passing, each node exchanges information (messages) with its neighboring nodes, allowing it to aggregate and update its own representation based on the information it receives. This iterative process allows information to propagate across the entire graph, enabling nodes to capture both local and global dependencies.
#gnn #graphneuralnetworks
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In many real-world applications, data can be naturally represented as graphs. For example, social networks, citation networks, molecular structures, and knowledge graphs all exhibit graph-like structures. GNNs have gained significant attention and popularity due to their ability to model and analyze such data effectively.
The core idea behind GNNs is to learn representations for nodes in a graph by leveraging both the local features of individual nodes and the graph's global structure. GNNs achieve this through a process called message passing. In message passing, each node exchanges information (messages) with its neighboring nodes, allowing it to aggregate and update its own representation based on the information it receives. This iterative process allows information to propagate across the entire graph, enabling nodes to capture both local and global dependencies.
#gnn #graphneuralnetworks
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