Semi-supervised learning with graphs… Semi-supervised learning addresses this problem by using … Because semi-supervised learning requires less human effort … We present a series of novel semi-supervised learning …LaffertyRosenfeld
本文关… 传奇电焊悍匪 [Defense] Graph Structure Learning for Robust Graph Neural Networks 论文链接,代码链接Abstract图神经网络 (GNN) 是图表示学习的强大工具。然而,最近的研究表明,GNN 容易受到精心设计的扰动,称为对抗性攻击。对抗性攻击很容易欺骗 GNN 对下游任务进行预测… 临时注册...
[4] Niepert M, Ahmed M, Kutzkov K. Learning convolutional neural networks for graphs[C]//International conference on machine learning. 2016: 2014-2023. [5] Yang Z, Cohen W W, Salakhutdinov R. Revisiting semi-supervised learning with graph embeddings[J]. arXiv preprint arXiv:1603.08861, 201...
[5]proposeaconvolutionalneuralnetworkthat operatesdirectlyongraphsandprovideanend-to-endfea- turelearningforgraphdata.AtwoodandTowsley[1]pro- poseDiffusion-ConvolutionalNeuralNetworks(DCNNs) byemployingagraphdiffusionprocesstoincorporatethe contextualinformationofnodeingraphnodeclassification. ∗ Corresponding...
Semi-Supervised Learning With Graphs 来自 Semantic Scholar 喜欢 0 阅读量: 782 作者: Xiaojin. Zhu 摘要: In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled instances however are often difficult, expensive, or time consuming to ...
4.1 Graph-based semi-supervised learning 4.2 Neural networks on graphs 五、Experiments 5.1 Datasets 数据集的信息表如图: (1)Citation networks 本文考虑三个引文网络数据集:Citeseer、Cora和PubMed(Sen等人,2008)。数据集包含每个文档的稀疏bag-of-words特征向量和文档之间的引用链接列表。本文将引用链接视为(无...
Consistency Regularization Loss同时保持S个增强数据对数据预测的一致性。假定S=2,可以通过最小化两个输出的L2距离 对于多个数据增强的情况,计算平均值,然后最小化各个数据增强与平均分布的差异 完整算法框架如下: 实验效果如下: 小结 这篇工作是基于图对比学习的思想对图半监督分类任务的尝试,总体工作在于其中的数据增...
Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on th... N Jia,X Tian,W Gao,... - 《Remote Sensing》 被引量: 0发...
· 论文解读《Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning》 · 论文解读二代GCN《Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering》 · 学习笔记:GCN · 图卷积网络 GCN · 【图算法】图卷积的演变-从谱图卷积到GCN 阅读排行: · 2025...
The five pairs of bar graphs in Fig.5 visualize the results. Table 1. AUC comparison for the fivebenchmark data sets (mean ± std) Graph sharpening removes noisy or unnecessary edges fromthe original graph. This enhances the robustness to noise in the dataset. Semi-supervised learningGraph...