GitHub

local-global-graph-transformer

local-global-graph-transformer/ ├── config/ │ ├── defaults.yaml # Edit simulation/training parameters here │ ├── paths.py # Automatic path management (linear/nonlinear) │ └── constants.py # Physical ...
IEEE

FLARE: Efficient Distributed Large-Scale Graph Neural Networks Training With Adaptive Latency-Aware Probabilistic Caching

Abstract: Since the emergence of Graph Neural Networks (GNNs), researchers have extensively investigated training on large-scale GNN training because of their success and wide usage in various domains ...
GitHub

Subgraph-conditioned Graph Information Bottleneck

We aim to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in ...
NPR

Together at last, Megadeth and Harry Styles rule the Billboard charts

The question is how well the song can sustain its first-week success, and early signs look good: It's the week's most-streamed track, and it debuts at No. 19 on the Radio Songs chart — the exact ...
MIT Technology Review

This is the most misunderstood graph in AI

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated. MIT Technology Review Explains: Let our writers untangle the complex, messy ...
IEEE

Optimization of Graph Neural Networks Training Using Graph Reordering

Abstract: Graph neural networks (GNNs) are specifically designed for graph-structured data and have gained significant attention. However, training GNN on large-scale graphs remains challenging due to ...