Graph Convolutional Neural Networks for Web-Scale Recommender Systems 主要内容概括: 1、 模型: 优化: 大部分译文: 摘要: 图结构的深度神经网络在推荐系统应用中最近已经有很好的表现。然而,使这些方法变得实用,对于拥有千万物品和一亿用户的网络推荐系统,还有挑战。我们阐述了一个发展和应用在Pinter
Inspired by image filters in standard convolutional neural networks (CNN), we propose a neural framework that connects bi-kernel with GNNs, incorporating predefined rules and focusing on the interpretability of graph filters during training. We address graph kernels based on their differentiability, ...
This allows Graph Neural Networks (GNNs) to be applied to broader unstructured domains such as 3D face analysis. GSL can be considered as the dynamic learning of connection weights within a layer of message passing in a GNN, and particularly in a Graph Convolutional Network (GCN). A ...
Kernel Graph Convolutional Neural Networks: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part IGraph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM ...
Kernel Graph Convolutional Neural Networks 摘要:图核已成功地应用于许多图分类问题中。通常,首先设计一个核,然后根据核隐式定义的特征训练SVM分类器。这种两阶段的方法将数据表示从学习中解耦,这是次优的。另一方面,卷积神经网络(CNNs)有能力在训练过程中直接从原始数据中学习自己的特征,但CNN不能处理图等不规则数...
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering Inductive Representation Learning on Large Graphs Semi-Supervised Classification with Graph Convolutional Networks Geometric deep learning on graphs and manifolds using mixture model CNNs ...
The initialization of Convolutional Neural Networks (CNNs) is about providing reasonable initial values for the convolution kernels and the fully connected layers. In this paper, we proposed a convolution kernel initialization method based on the two-dimensional principal component analysis (2DPCA), in...
Graphconvolutionalnetworksgainremarkablesuccessinsemi-supervisedlearningongraph-structureddata.Thekeytograph-basedsemi-supervisedlearningiscapturingthesmoothnessoflabelsorfeaturesovernodesexertedbygraphstructure.Previousmethods,spectralmethodsandspatialmethods,devotetode,ninggraphcon-volutionasaweightedaverageoverneighboringnodes...
在这篇论文中,作者提出了一种更加通用的池化框架,以核函数的形式捕捉特征之间的高阶信息。同时也证明了使用无参数化的紧致清晰特征映射,以指定阶形式逼近核函数,例如高斯核函数。本文提出的核函数池化可以和CNN网络联合优化。 Network Structure Overview Kernel Poolin
In this paper, we propose a novel multi-modal graph convolutional neural network (M2GCN) for link prediction in multi-modal networks which consist of ... Q Liu,E Yao,YXM Liu - Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving...