EurekAlert!

Real-time, large-scale graph neural network inference through BingoCGN

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 ...
Nature

Fairness-Aware Graph Neural Networks and Social Influence Maximization

Graph Neural Networks (GNNs) have become a central tool for learning from network-structured data, excelling in tasks such as node classification, link prediction and representation learning. In ...
EurekAlert!

Simplified multi-view graph neural network for multilingual knowledge graph completion

Knowledge graph completion (KGC) aims to fill in missing entities and relations within knowledge graphs (KGs) to address their incompleteness. Most existing KGC models suffer from knowledge coverage ...
Finextra

Graph Neural Networks and GraphRAG: Navigating Open-World Complexity in Finance

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 ...
Hosted on MSN

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 ...