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Train sparse GNNs with dynamic graph topologies across epochs without re-extracting structure, achieving state-of-the-art performance even at extreme edge budgets.
A unified layer equation for GNNs reveals how over 200 architectures can be systematically compared and optimized for performance.
Personalization in AI co-scientists could be the key to unlocking novel research insights that generic systems overlook.
