整体进度：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
项目仓库：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）
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
- 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
- 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
- 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?
- 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
- Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification
- Geniepath: Graph neural networks with adaptive receptive paths
- Hypergraph Neural Networks
- Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
- Can GCNs Go as Deep as CNNs?
- Graph Attention
- Graph Attention Networks
- Gaan: Gated attention networks for learning on large and spatiotemporal graphs
- 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
- Link Prediction Based on Graph 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，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
-
forkGithub 项目 - 将文档（Markdown 格式）放在
docs中。 pushpull request
请见 Github 入门指南。
