#graph neural networks

Discover DeepMind's Graph Nets library designed for creating graph networks using TensorFlow and Sonnet. Easily accessible through pip, the library is compatible with both CPU and GPU versions of TensorFlow. Graph Nets enable efficient graph neural network development, supporting graph-structured data processing. Interactive Jupyter demos illustrate its application in shortest path, sorting, and physics tasks. This library is well-suited for those interested in harnessing the adaptability and strength of graph networks in complex data modeling.

With a generative model that enables programmable protein structures, this method efficiently generates all-atom designs suitable for sequence generation. It incorporates diffusion modeling and equivariant neural networks to enhance flexibility and customization in addressing structural constraints. The approach supports the creation of large, complex structures on commodity GPUs, adapting to constraints like substructure and symmetry while allowing customization through user-defined conditioners.

Graph-Learn, previously AliGraph, is a distributed framework designed for developing large-scale graph neural networks. Utilized in applications like search recommendations and network security at Alibaba, it combines online inference services with training, providing a full solution for GNN deployments in commercial contexts. GraphLearn-Training enables graph sampling and GNN model training with interfaces in Python and C++, supporting TensorFlow and PyTorch. The Dynamic-Graph-Service offers real-time sampling with latency assurances on dynamic graphs, advancing online inference potential.