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Neural Message Passing is a technique used in graph neural networks where nodes in a graph iteratively exchange and update information with their neighbors to learn representations. This method enables the model to capture complex dependencies and relational information inherent in graph-structured data, making it powerful for tasks like node classification and link prediction.
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