- 기간: 2022.08.01 ~
- 내용: cs224w 강의
- Code
- Lecture
- Code
- Lecture
- Lecture
- Lecture
- 6.1 - Introduction to Graph Neural Networks
- 6.2 - Basics of Deep Learning
- 6.3 - Deep Learning for Graphs
- 7.1 - A general Perspective on GNNs
- 7.2 - A Single Layer of a GNN
- 7.3 - Stacking layers of a GNN
- 8.1 - Graph Augmentation for GNNs
- 8.2 - Training Graph Neural Networks
- 8.3 - Setting up GNN Prediction Tasks
- 9.1 - How Expressive are Graph Neural Networks
- 9.2 - Designing the Most Powerful GNNs
- Code
- cs224w HW colab 2
- GCN
- cs224w HW colab 3
- GraphSAGE
- GAT
- cs224w HW colab 2
- Summary
- Paper
- Design Space of Graph Neural Networks, NIPS 2020
GCN: Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017GraphSAGE: Inductive Representation Learning on Large Graphs, NIPS 2017GAT: Graph Attention Networks, ICLR 2018Batch Normalization: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, ICML 2015Dropout: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JMLR 2014Skip connection: Deep Residual Learning for Image RecognitionSkip connection in Graph: Representation Learning on Graphs with Jumping Knowledge Networks, ICML 2018GIN: HOW POWERFUL ARE GRAPH NEURAL NETWORKS?, ICLR 2019
- Lecture
- Paper
TranE: Translating Embeddings for Modeling Multi-relational DataTranR: Learning Entity and Relation Embeddings for Knowledge Graph CompletionDistMult: Embedding Entities and Relations for Learning and Inference in Knowledge BasesComplEx: Complex Embeddings for Simple Link Prediction- Traversing Knowledge Graphs in Vector Space
- Embedding Logical Queries on Knowledge Graphs
- Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
- Lecture
- Paper
- Lecture
- Fast Neural Subgraph Matching and Counting
- GNNs for Recommender Systems
- Community Detection in Networks
- Deep Generative Models for Graphs
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent