Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...
Dynamic graph representation serves as a framework for modelling systems whose structure evolves over time, incorporating changes in nodes and edges to capture temporal patterns. Link prediction ...
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AI Researchers Are Confronting the Gap Between Neural Network Power and True Generalization
In 2026, neural networks are achieving unprecedented capabilities across industries, yet large-scale tests reveal persistent struggles with generalization. Researchers are exploring adaptive ...
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