Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...
Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...
MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...
BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...
SM-GNN prunes multi-view GNNs to pure propagation, cutting training time while outperforming prior MKGC accuracies on two ...
Scholars deliver the first systematic survey of Dynamic GNNs, unifying continuous- and discrete-time models, benchmarking ...
SGR turns adversarial shortcuts into teachers, isolating invariant subgraph rationales and delivering the first XGL framework ...
Representing a molecule in a way that captures both its structure and function is central to tasks such as molecular property prediction, drug drug ...
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