As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...
AIFU stock rated Sell: earnings declines, negative technicals, and China insurance commission caps pressure margins. Click ...
The Green party has made gains under its leader – but there is also uncertainty ahead ...
Herbalife (NYSE:HLF) markets a portfolio centred on nutrition and personal care, including protein shakes, vitamins, energy ...
From the Third Intermediate Period through the Late, Ptolemaic, and Roman eras, ancient Egyptian private funerary monuments underwent marked architectural and ritual reconfigurations ...
Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract ...
Abstract: The task of network alignment aims to identify corresponding nodes across multiple networks, with applications in various fields such as social network analysis and bioinformatics.
Physicists have long recognized the value of photonic graph states in quantum information processing. However, the difficulty ...
Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...
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