Graph Neural Networks

Graph Neural Networks (GNNs) have come a long way since they solved Cora and PubMed node classification. But what happens when you want to model an entire traffic network, a biomedical knowledge graph, or a social graph with billions of nodes? That’s where PyG 2.0 steps in. The Industrialization of GNNs PyTorch Geometric (PyG) has been a dominant tool in the academic development of GNNs. With PyG 2.0, it graduates into the world of industrial-strength machine learning. This isn’t just a library update—it’s a fundamental re-architecture with three goals: ...

Can a model trained to think like a day trader ever truly understand long-term market moves? Most financial AI systems today seem stuck in the equivalent of high-frequency tunnel vision — obsessed with predicting tomorrow’s returns and blind to the richer patterns that shape actual investment outcomes. A new paper, NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction, proposes a more grounded solution. It redefines the task itself, the architecture behind the prediction, and how we should even build the graphs powering these systems. ...