Abstract: Graph Neural Networks (GNNs) exploit topological structures—namely, node-to-node connections—to aggregate contextual information, thereby achieving strong performance across diverse domains.
Abstract: Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or minimally ...
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