Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...
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 ...
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 ...
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 ability to create Graph Neural Networks within the AllegroGraph platform opens up the next level of AI to data analytics professionals with the ability to produce the best prescriptive outcomes,” ...
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 ...
As one of the most crucial topics in the recommendation system field, Point-of-Interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks ...
Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...
The Optum Enterprise and Data Analytics (EDA) Graph & Health @ Scale (GHS) team is happy to announce v1.0 of our g2gnn library. In this presentation we will discuss the g2gnn library, how it works, ...
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