Graph Networks

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Graph networks, often called graph neural networks (GNNs), are deep learning models designed to work with data that is naturally organized as a graph. This includes things like social networks, transportation networks, or knowledge graphs. Instead of treating each data point on its own, GNNs look at nodes, the connections between them, and the structure formed by those connections.

GNNs use a process often called message passing, where each node gathers information from its neighbors and updates its own internal state. After several rounds of this, the network learns patterns that reflect both the local neighborhood around each node and the larger structure of the entire graph. This makes GNNs well-suited for tasks where relationships matter, such as classifying nodes or helping scientists analyze molecules and proteins. Their design respects how graphs work in the real world, so they often perform better than standard neural networks on graph-shaped data.

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