CS224W摘要总纲（已完结）
文章目录
- [01.Introduction；Machine Learning for Graphs](https://blog.csdn.net/oldmao_2001/article/details/120067969)
- [02.Traditional Methods for Machine Learning in Graphs](https://blog.csdn.net/oldmao_2001/article/details/120119282)
- [03.Node Embedding](https://blog.csdn.net/oldmao_2001/article/details/120247466)
- [04.Link Analysis: PageRank](https://blog.csdn.net/oldmao_2001/article/details/120253335)
- [05.Label Propagation for Node Classification](https://blog.csdn.net/oldmao_2001/article/details/120310357)
- 06.07.GNN
- [08.Applications of Graph Neural Networks](https://blog.csdn.net/oldmao_2001/article/details/120403754)
- [09.Theory of Graph Neural Networks](https://blog.csdn.net/oldmao_2001/article/details/120468742)
- [10.Knowledge Graph Embeddings](https://blog.csdn.net/oldmao_2001/article/details/120475981)
- [11.Reasoning over Knowledge Graphs](https://blog.csdn.net/oldmao_2001/article/details/120509854)
- [12.Frequent Subgraph Mining with GNNs](https://blog.csdn.net/oldmao_2001/article/details/120567527)
- [13.Community Detection in Networks](https://blog.csdn.net/oldmao_2001/article/details/120607166)
- [14.Traditional Generative Models for Graphs](https://blog.csdn.net/oldmao_2001/article/details/120642481)
- [15.Deep Generative Models for Graphs](https://blog.csdn.net/oldmao_2001/article/details/120673776)
- [16.Advanced Topics on GNNs](https://blog.csdn.net/oldmao_2001/article/details/120700381)
- [17.Scaling Up GNNs](https://blog.csdn.net/oldmao_2001/article/details/120730080)
- [Graph Neural Networks in Computational Biology](https://blog.csdn.net/oldmao_2001/article/details/120810623)
- [19.GNNs for Science](https://blog.csdn.net/oldmao_2001/article/details/120905267)
CS224W: Machine Learning with Graphs
公式输入请参考： 在线Latex公式
01.Introduction；Machine Learning for Graphs
Why Graphs?
Applications of Graph ML
Choice of Graph Representation
02.Traditional Methods for Machine Learning in Graphs
Node-level Tasks and Features
Link Prediction Task and Feature
Graph-Level Features and Graph Kernels
03.Node Embedding
Encoder 和Decoder框架
随机游走
Node2Vec
Embedding entire graphs
04.Link Analysis: PageRank
PageRank介绍
How to solve PageRank?
Random Walk with restarts and Personalized PageRank
Matrix Factorization and Node Embeddings
Limitations of node embeddings via matrix factorization and random walks
05.Label Propagation for Node Classification
Homophily和Influence
Probabilistic Relational classifiers
Iterative classification
Belief Propagation
06.07.GNN
Basic of Deep Learning（略）
Deep Learning for Graphs
08.Applications of Graph Neural Networks
Graph Feature augmentation
Graph Structure augmentation
Prediction with GNNs
Training Graph Neural Networks
Setting-up GNN Prediction Tasks
09.Theory of Graph Neural Networks
How powerful are GNNs?
local neighborhood structures
lComputational graph
Injective Function
lDesigning the Most Powerful Graph Neural Network
linjective multiset function
lGIN vs WL graph Kernel
10.Knowledge Graph Embeddings
Heterogeneous Graphs and Relational GCN (RGCN)
Knowledge Graphs: KG Completion with Embeddings
Knowledge Graph Completion: TransE, TransR, DistMult, ComplEx
11.Reasoning over Knowledge Graphs
Reasoning over Knowledge Graphs
Answering Predictive Queries on Knowledge Graphs
Query2box: Reasoning over KGs Using Box Embeddings
AND-OR queries
12.Frequent Subgraph Mining with GNNs
Subgraph and Motifs
Neural Subgraph Matching / Representations
Mining / Finding Frequent Motifs / Subgraphs
13.Community Detection in Networks
Community Detection in Networks
Network Communites
Louvain Algorithm
Detecting Overlapping Communities: BigCLAM
14.Traditional Generative Models for Graphs
Properties of Real-world Graphs
MSN Graph
Erdös-Renyi Random Graphs
The Small-World Model
Kronecker Graph Model
15.Deep Generative Models for Graphs
Graph Generative Models
GraphRNN
深度图生成模型的应用（药物）
16.Advanced Topics on GNNs
Limitations of GNN
Position-aware GNNs
Identity-aware GNNs
GNN的鲁棒性
17.Scaling Up GNNs
Scaling Up GNNs to Large Graphs
Neighbor Sampling: GraphSAGE
Cluster-GCN
Simplified GCN
Graph Neural Networks in Computational Biology
背景介绍
药物及药物组合的安全性
患者预后及疾病分类
新药研发
19.GNNs for Science
Pre-Training GNN
Hyperbolic Graph Embeddings
Design Space of GNN