Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...
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 ...
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 ...
Representing a molecule in a way that captures both its structure and function is central to tasks such as molecular property prediction, drug drug ...
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 ...
A technical paper titled “SCAR: Power Side-Channel Analysis at RTL-Level” was published by researchers at University of Texas at Dallas, Technology Innovation Institute and University of Illinois ...
A new technical paper titled “TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs” was published by researchers at University of Connecticut and University of Minnesota. “hip ...
Scholars deliver the first systematic survey of Dynamic GNNs, unifying continuous- and discrete-time models, benchmarking ...
Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
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