Graph neural networks are a versatile machine learning architecture that received a lot of attention recently due to its wide range of applications. In this technical report, we present an implementation of grap
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform. It provides... a tfgnn.GraphTensor type to represent graphs with a heterogeneous schema, that is, multiple types of nodes and edges; tools for data preparation, notably a graph sampler to convert a huge ...
Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predictin...
MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and KerasGraph neural networksMachine learningDeep learningCheminformaticsBioinformaticsMolecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting...
定义tensorflow的palceholder用于数据输入。 def convergence(a, state, old_state, k): with tf.variable_scope('Convergence'): # assign current state to old state old_state = state # 获取子结点上一个时刻的状态 # grub states of neighboring node gat = tf.gather(old_state, tf.cast(a[:, 0]...
GRAPH ATTENTION NETWORKS 文章来源:ICLR2018 下载地址:GRAPH ATTENTION NETWORKS 论文源码 Tensorflow:github.com/PetarV-/GAT Pytorch:github.com/Diego999/pyG 论文摘要 本文提出了图注意网络(GATs),它对图结构数据进行操作,并且使用了masked self-attentional layer。网络中的图注意力层的计算效率非常高(不需要矩阵计...
Graph neural networks in tensorflow and keras with spektral [application notes]. IEEE Comput. Intell. Mag. 16, 99–106 (2021). Article Google Scholar Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference...
同样,如果你更熟悉 TensorFlow 和 Keras,Spektral可能会更有价值。如果你想使用新兴的 JAX 生态系统进行开发,那么Jraph可能非常适合你的 GNN 项目。 当然,如果你的团队更喜欢 Julia 而不是 Python,你可能更希望着眼于GeometricFlux.jl或GraphNeuralNetworks.jl,它们都基于 Flux.jl 机器学习生态系统。
在理论分析的基础上,我们提出了一个新的基线框架gfNN(graph filter neural network)来实证分析顶点分类问题。gfNN包括两个步骤:1。用图过滤矩阵相乘的方法过滤特征。利用机器学习模型学习顶点标签。我们使用下图中的一个简单实现模型来演示我们的框架的有效性。
同样,如果你更熟悉 TensorFlow 和 Keras,Spektral可能会更有价值。如果你想使用新兴的 JAX 生态系统进行开发,那么Jraph可能非常适合你的 GNN 项目。 当然,如果你的团队更喜欢 Julia 而不是 Python,你可能更希望着眼于GeometricFlux.jl或GraphNeuralNetworks.jl,它们都基于 Flux.jl 机器学习生态系统。