google-deepmind/graph_nets
Graph Nets
graph_nets est un framework et une bibliothèque d'apprentissage profond structuré sur graphes pour construire des réseaux de neurones à passage de messages. Il fournit des outils pour concevoir des architectures qui opèrent sur des nœuds et des arêtes pour traiter et raisonner sur des données structurées en graphes en utilisant TensorFlow.
Le framework implémente un paradigme de passage de messages pour l'échange d'informations itératif entre les nœuds. Cette approche permet le développement de modèles capables de raisonner sur des entrées complexes structurées en graphes pour des tâches telles que la recherche de chemin et le tri, ou de servir de prédicteur pour les états futurs et les trajectoires de systèmes physiques.
Features
- Graph Message Passing Frameworks - Provides a framework for implementing message passing primitives to learn representations of nodes and edges in graphs.
- Geometric Deep Learning Frameworks - Provides a framework for applying deep learning to non-Euclidean data such as molecular graphs.
- Graph Neural Networks - Provides a library for building neural network architectures designed to process data represented as graphs.
- Message Passing Primitives - Implements differentiable compute patterns for aggregating and updating features across graph topologies.
- TensorFlow Graph Execution - Utilizes TensorFlow's computational graphs to execute mathematical operations on hardware accelerators.
- Graph Representations - Provides data structures that store graph topology and edge attributes for neural network processing.
- Fixed-Point Convergence - Processes graph information repeatedly until node and edge representations reach a stable state.
- Graph Reasoning Systems - Implements systems for reasoning over graph-structured data to perform tasks like path-finding and sorting.
- Iterative Prediction Refiners - Feeds model outputs back as inputs to iteratively refine predictions and simulate system evolution.
- Physical System Trajectories - Forecasts the future state of a physical system by iteratively feeding model predictions back as inputs.
- Graph Computation - Implements graph-based computation to perform complex tasks such as path-finding or sorting.
- Physical System Trajectory Predictors - Iteratively predicts the future state of physical systems by processing graph-based spatial data.
