Graph Nets

graph_nets 是一个图结构深度学习框架和库，用于构建消息传递神经网络。它提供了用于设计架构的工具，这些架构在节点和边上运行，以使用 TensorFlow 处理和推理图结构数据。

该框架实现了用于节点间迭代信息交换的消息传递范式。这种方法使得开发能够推理复杂图结构输入的模型成为可能，适用于路径查找和排序等任务，或作为物理系统未来状态和轨迹的预测器。

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.

Star 历史

google-deepmindgraph_nets

Name: google-deepmind/graph_nets
Author: google-deepmind

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5,402 星标778 分支PythonApache-2.05 次浏览arxiv.org/abs/1806.01261

Graph Nets

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.

Graph Nets 的开源替代方案

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常见问题解答

google-deepmind/graph_nets 是做什么的？

google-deepmind/graph_nets 的主要功能有哪些？

google-deepmind/graph_nets 的主要功能包括：Graph Message Passing Frameworks, Geometric Deep Learning Frameworks, Graph Neural Networks, Message Passing Primitives, TensorFlow Graph Execution, Graph Representations, Fixed-Point Convergence, Graph Reasoning Systems。

google-deepmind/graph_nets 有哪些开源替代品？

google-deepmind/graph_nets 的开源替代品包括： dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… rusty1s/pytorch_geometric — PyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides… tkipf/pygcn — pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for… pageman/sutskever-30-implementations — This project is a collection of deep learning research implementations and a reproduction kit designed to translate… packtpublishing/hands-on-graph-neural-networks-using-python — This repository serves as an educational resource for implementing graph neural networks using Python. It provides a… charlesq34/pointnet2 — PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a…