现有的基于谱的图卷积网络模型有以下这些:Spectral CNN、Chebyshev Spectral CNN (ChebNet)、Adaptive Graph Convolution Network (AGCN) 基于谱的图卷积神经网络方法的一个常见缺点是,它们需要将整个图加载到内存中以执行图卷积,这在处理大型图时是不高效的。 1.2 Spatial-based Graph Convolutional Networks 基于空间的...
受深度学习成功的启发,研究者们采用卷积神经网络(convolutional neural network, GCN)开发了图卷积网络(Graph convolutional Networks, GCN),并通过考虑拓扑信息和采用全连接网络(fully connected network, FCN)取得了惊人的分类精度。然而,如果直接使用给定的网络拓扑结构进行分类,也可能会导致性能下降,因为它可能具有较高...
因此,本文试图沿着图神经网络的历史脉络,从最早基于不动点理论的图神经网络(Graph Neural Network, GNN)一步步讲到当前用得最火的图卷积神经网络(Graph Convolutional Neural Network, GCN), 期望通过本文带给读者一些灵感与启示。 本文的提纲与叙述要点主要参考了3篇图神经网络的Survey,分别是来自IEEE Fellow的A Comp...
现有的基于频谱的图卷积网络模型有以下这些:Spectral CNN、Chebyshev Spectral CNN (ChebNet)、Adaptive Graph Convolution Network (AGCN)基于频谱的图卷积神经网络方法的一个常见缺点是,它们需要将整个图加载到内存中以执行图卷积,这在处理大型图时是不高效的。1.2 Spatial-based Graph Convolutional Networks模拟传统卷积...
The feature decoder portion of theforwardmethod for this neural network is defined below: # Loop through each convolutional layer within decoder for i, conv in enumerate(self.decoder_convs): # Use outputs from encoder as inputs to feature decoder ...
a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmen...
Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such techniques explore the analogy between the graph Laplacian eigenvectors and the classical Fourier basis, allowing to formulate the co...
Edge Attention-based Multi-Relational Graph Convolutional Networks (2018) Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi Paper:https://arxiv.org/abs/1802.04944v1 Python Reference:https://github.com/Luckick/EAGCN ...
When does self-supervision help graph convolutional networks?. ICML, 2020. Self-supervised learning on graphs: Deep insights and new direction. arxiv preprint. Strategies for pre-training graph neural networks. ICLR, 2020. Graph-based neural network models with multiple self-supervised auxiliary tasks...
a computational RBP binding site prediction framework based on graph convolutional neural networks (GCNs). In contrast to current CNN methods, GraphProt2 offers native support for the encoding of base pair information as well as variable length input, providing increased flexibility and the prediction...