图深度学习论文笔记整理活动 | ApacheCN

左手的ㄟ右手 2021-11-27 04:50 247阅读 0赞

整体进度：[https://github.com/apachecn/graph-emb-dl-notes/issues/1][https_github.com_apachecn_graph-emb-dl-notes_issues_1]

贡献指南：[https://github.com/apachecn/graph-emb-dl-notes/blob/master/CONTRIBUTING.md][https_github.com_apachecn_graph-emb-dl-notes_blob_master_CONTRIBUTING.md]

项目仓库：[https://github.com/apachecn/graph-emb-dl-notes][https_github.com_apachecn_graph-emb-dl-notes]

--------------------

# 贡献指南 #

> 请您勇敢地去翻译和改进翻译。虽然我们追求卓越，但我们并不要求您做到十全十美，因此请不要担心因为翻译上犯错——在大部分情况下，我们的服务器已经记录所有的翻译，因此您不必担心会因为您的失误遭到无法挽回的破坏。（改编自维基百科）

负责人：

*  [飞龙][Link 1]：562826179

## 章节列表 ##

*  GCN
    
     *  [A new model for learning in graph domains][]
     *  [The graph neural network model][]
     *  [Spectral networks and locally connected networks on graphs][]
     *  [Convolutional networks on graphs for learning molecular fingerprints][]
     *  [Gated graph sequence neural networks][]
     *  [Accelerated filtering on graphs using lanczos method][]
     *  [Deep convolutional networks on graph-structured data][]
     *  [Convolutional neural networks on graphs with fast localized spectral filtering][]
     *  [Diffusion-convolutional neural networks][]
     *  [Learning convolutional neural networks for graphs][]
     *  [Molecular graph convolutions: moving beyond fingerprints][Molecular graph convolutions_ moving beyond fingerprints]
     *  [Inductive representation learning on large graphs][]
     *  [Neural message passing for quantum chemistry][]
     *  [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs][]
     *  [Geometric deep learning on graphs and manifolds using mixture model cnns][]
     *  [Semi-supervised classification with graph convolutional networks][]
     *  [Robust spatial filtering with graph convolutional neural networks][]
     *  [Cayleynets: graph convolutional neural networks with complex rational spectral filters][Cayleynets_ graph convolutional neural networks with complex rational spectral filters]
     *  [Hierarchical graph representation learning with differentiable pooling][]
     *  [Structure-Aware Convolutional Neural Networks][]
     *  [Adaptive graph convolutional neural networks][]
     *  [Deeper insights into graph convolutional networks for semi-supervised learning][]
     *  [Large-Scale Learnable Graph Convolutional Networks][]
     *  [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling][FastGCN_ Fast Learning with Graph Convolutional Networks via Importance Sampling]
     *  [Learning steady-states of iterative algorithms over graphs][]
     *  [Representation learning on graphs with jumping knowledge networks][]
     *  [Stochastic Training of Graph Convolutional Networks with Variance Reduction][]
     *  [Dual graph convolutional networks for graph-based semi-supervised classification][]
     *  [Graph capsule convolutional neural networks][]
     *  [How powerful are graph neural networks?][How powerful are graph neural networks]
     *  [Modeling relational data with graph convolutional networks][]
     *  [Multidimensional graph convolutional networks][]
     *  [Signed graph convolutional network][]
     *  [Capsule Graph Neural Network][]
     *  [Combining Neural Networks with Personalized PageRank for Classification on Graphs][]
     *  [DIFFUSION SCATTERING TRANSFORMS ON GRAPHS][]
     *  [Graph Wavelet Neural Network][]
     *  [LanczosNet: Multi-Scale Deep Graph Convolutional Networks][LanczosNet_ Multi-Scale Deep Graph Convolutional Networks]
     *  [Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification][]
     *  [Geniepath: Graph neural networks with adaptive receptive paths][Geniepath_ Graph neural networks with adaptive receptive paths]
     *  [Hypergraph Neural Networks][]
     *  [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks][Weisfeiler and Leman Go Neural_ Higher-order Graph Neural Networks]
     *  [Can GCNs Go as Deep as CNNs?][Can GCNs Go as Deep as CNNs]
 *  Graph Attention
    
     *  [Graph Attention Networks][]
     *  [Gaan: Gated attention networks for learning on large and spatiotemporal graphs][Gaan_ Gated attention networks for learning on large and spatiotemporal graphs]
     *  [Watch your step: Learning node embeddings via graph attention][Watch your step_ Learning node embeddings via graph attention]
     *  [Graph classification using structural attention][]
 *  GAE
    
     *  [Structural deep network embedding][]
     *  [Deep neural networks for learning graph representations][]
     *  [Variational graph auto-encoders][]
     *  [Mgae: Marginalized graph autoencoder for graph clustering][Mgae_ Marginalized graph autoencoder for graph clustering]
     *  [Link Prediction Based on Graph Neural Networks][]
     *  [SpectralNet: Spectral Clustering using Deep Neural Networks][SpectralNet_ Spectral Clustering using Deep Neural Networks]
     *  [Deep Recursive Network Embedding with Regular Equivalence][]
     *  [Learning Deep Network Representations with Adversarially Regularized Autoencoders][]
     *  [Adversarially Regularized Graph Autoencoder for Graph Embedding][]
     *  [Deep graph infomax][]

## 流程 ##

### 一、认领 ###

首先查看[整体进度][https_github.com_apachecn_graph-emb-dl-notes_issues_1]，确认没有人认领了你想认领的章节。

然后回复 ISSUE，注明“章节 + QQ 号”。

### 二、整理笔记 ###

阅读论文，填写以下内容：

*  模型架构
 *  输入类型：同构图/二分图
 *  嵌入类型：节点/边/子图/整图
 *  任务类型：无监督/半监督
 *  和 baseline 相比的创新点
 *  （有/无）理论解释

### 三、提交 ###

*  `fork` Github 项目
 *  将文档（**Markdown 格式**）放在`docs`中。
 *  `push`
 *  `pull request`

请见 [Github 入门指南][Github]。

[https_github.com_apachecn_graph-emb-dl-notes_issues_1]: https://github.com/apachecn/graph-emb-dl-notes/issues/1
[https_github.com_apachecn_graph-emb-dl-notes_blob_master_CONTRIBUTING.md]: https://github.com/apachecn/graph-emb-dl-notes/blob/master/CONTRIBUTING.md
[https_github.com_apachecn_graph-emb-dl-notes]: https://github.com/apachecn/graph-emb-dl-notes
[Link 1]: https://github.com/wizardforcel
[A new model for learning in graph domains]: https://ieeexplore.ieee.org/abstract/document/1555942
[The graph neural network model]: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1015.7227&rep=rep1&type=pdf
[Spectral networks and locally connected networks on graphs]: https://arxiv.org/pdf/1312.6203.pdf
[Convolutional networks on graphs for learning molecular fingerprints]: http://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf
[Gated graph sequence neural networks]: https://arxiv.org/pdf/1511.05493.pdf
[Accelerated filtering on graphs using lanczos method]: https://arxiv.org/pdf/1509.04537.pdf
[Deep convolutional networks on graph-structured data]: https://arxiv.org/abs/1506.05163
[Convolutional neural networks on graphs with fast localized spectral filtering]: https://arxiv.org/pdf/1606.09375.pdf
[Diffusion-convolutional neural networks]: https://arxiv.org/pdf/1511.02136.pdf
[Learning convolutional neural networks for graphs]: https://arxiv.org/pdf/1605.05273.pdf
[Molecular graph convolutions_ moving beyond fingerprints]: https://arxiv.org/pdf/1603.00856.pdf
[Inductive representation learning on large graphs]: http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf
[Neural message passing for quantum chemistry]: https://arxiv.org/pdf/1704.01212.pdf
[Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs]: https://arxiv.org/pdf/1704.02901.pdf
[Geometric deep learning on graphs and manifolds using mixture model cnns]: https://arxiv.org/pdf/1611.08402.pdf
[Semi-supervised classification with graph convolutional networks]: https://arxiv.org/pdf/1609.02907.pdf
[Robust spatial filtering with graph convolutional neural networks]: https://arxiv.org/abs/1703.00792
[Cayleynets_ graph convolutional neural networks with complex rational spectral filters]: https://arxiv.org/pdf/1705.07664.pdf
[Hierarchical graph representation learning with differentiable pooling]: https://arxiv.org/pdf/1806.08804.pdf
[Structure-Aware Convolutional Neural Networks]: http://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks.pdf
[Adaptive graph convolutional neural networks]: https://arxiv.org/pdf/1801.03226.pdf
[Deeper insights into graph convolutional networks for semi-supervised learning]: https://arxiv.org/pdf/1801.07606.pdf
[Large-Scale Learnable Graph Convolutional Networks]: https://arxiv.org/pdf/1808.03965.pdf
[FastGCN_ Fast Learning with Graph Convolutional Networks via Importance Sampling]: https://arxiv.org/pdf/1801.10247.pdf
[Learning steady-states of iterative algorithms over graphs]: http://proceedings.mlr.press/v80/dai18a/dai18a.pdf
[Representation learning on graphs with jumping knowledge networks]: https://arxiv.org/pdf/1806.03536.pdf
[Stochastic Training of Graph Convolutional Networks with Variance Reduction]: https://arxiv.org/pdf/1710.10568.pdf
[Dual graph convolutional networks for graph-based semi-supervised classification]: http://delivery.acm.org/10.1145/3190000/3186116/p499-zhuang.pdf?ip=1.129.110.137&id=3186116&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1546208231_ba22bb40f3bc41441d1fea0606eb8adb
[Graph capsule convolutional neural networks]: https://arxiv.org/abs/1805.08090
[How powerful are graph neural networks]: https://arxiv.org/pdf/1810.00826.pdf
[Modeling relational data with graph convolutional networks]: https://arxiv.org/pdf/1703.06103.pdf
[Multidimensional graph convolutional networks]: https://arxiv.org/pdf/1808.06099.pdf
[Signed graph convolutional network]: https://arxiv.org/pdf/1808.06354.pdf
[Capsule Graph Neural Network]: https://openreview.net/pdf?id=Byl8BnRcYm
[Combining Neural Networks with Personalized PageRank for Classification on Graphs]: https://openreview.net/pdf?id=H1gL-2A9Ym
[DIFFUSION SCATTERING TRANSFORMS ON GRAPHS]: https://arxiv.org/pdf/1806.08829.pdf
[Graph Wavelet Neural Network]: https://openreview.net/pdf?id=H1ewdiR5tQ
[LanczosNet_ Multi-Scale Deep Graph Convolutional Networks]: https://openreview.net/pdf?id=BkedznAqKQ
[Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification]: https://arxiv.org/pdf/1811.11103.pdf
[Geniepath_ Graph neural networks with adaptive receptive paths]: https://arxiv.org/pdf/1802.00910.pdf
[Hypergraph Neural Networks]: https://arxiv.org/pdf/1809.09401.pdf
[Weisfeiler and Leman Go Neural_ Higher-order Graph Neural Networks]: https://arxiv.org/pdf/1810.02244.pdf
[Can GCNs Go as Deep as CNNs]: https://arxiv.org/abs/1904.03751
[Graph Attention Networks]: https://arxiv.org/pdf/1710.10903.pdf
[Gaan_ Gated attention networks for learning on large and spatiotemporal graphs]: https://arxiv.org/pdf/1803.07294.pdf
[Watch your step_ Learning node embeddings via graph attention]: https://arxiv.org/pdf/1710.09599.pdf
[Graph classification using structural attention]: https://dl.acm.org/citation.cfm?id=3219980
[Structural deep network embedding]: https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf
[Deep neural networks for learning graph representations]: https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12423/11715
[Variational graph auto-encoders]: https://arxiv.org/pdf/1611.07308.pdf
[Mgae_ Marginalized graph autoencoder for graph clustering]: https://shiruipan.github.io/pdf/CIKM-17-Wang.pdf
[Link Prediction Based on Graph Neural Networks]: https://arxiv.org/pdf/1802.09691.pdf
[SpectralNet_ Spectral Clustering using Deep Neural Networks]: https://arxiv.org/pdf/1801.01587.pdf
[Deep Recursive Network Embedding with Regular Equivalence]: http://cuip.thumedialab.com/papers/NE-RegularEquivalence.pdf
[Learning Deep Network Representations with Adversarially Regularized Autoencoders]: http://www.cs.ucsb.edu/~bzong/doc/kdd-18.pdf
[Adversarially Regularized Graph Autoencoder for Graph Embedding]: https://www.ijcai.org/proceedings/2018/0362.pdf
[Deep graph infomax]: https://arxiv.org/abs/1809.10341
[Github]: https://github.com/apachecn/kaggle/blob/master/docs/GitHub