PyTorch中的RAHGCN 1.概述 该存储库是PyTorch中通过强化学习（RAHGCN）实现的自适应双曲图卷积神经网络。 下游任务包括： 链接预测（ lp ） 节点分类（ nc ） 2.设定 2.1下载代码 首先从Github下载源代码。 git clone git@github.com:fuxingcheng/RHGNN.git" cd rahgcn 2.2启动虚拟环境 我们建议在虚拟环境中设置我们的项目。 您可以选择conda或virtualenv来创建和管理虚拟环境。 如果您尚未安装conda，请按照的说明进行安装。 如果尚未安装virtualenv，则只需运行pip3 install virtualenv 。 对于conda ： conda env create -f environment.yml python=3.6 source activate ra

更新：签出库 ，它在一个漂亮干净的Python包中重新实现了学习分支所需的一切（位于）。 图卷积神经网络的精确组合优化 Maxime Gasse，DidierChételat，Nicola Ferroni，Laurent Charlin，Andrea Lodi 这是我们NeurIPS 2019的正式实施。 安装 请参阅安装说明。 运行实验 设置覆盖 # Generate MILP instances python 01_generate_instances.py setcover # Generate supervised learning datasets python 02_generate_samples.py setcover -j 4 # number of available CPUs # Training for i in {0..4} do python 03_

专利

图卷积神经网络及其应用，来自中科院计算所沈华伟博士在ICLR 2019顶会上的演讲稿，欢迎大家下载学习。

基于图卷积神经网络的软件定义电力通信网络路由控制策略.pdf

基于神经网络的SLAM顶会论文

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.

STGCN图卷积神经网络

图卷积神经网络从入门到实战

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. 图卷积神经网络，2018年AAAI论文代码。