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CS224W:
Machine Learning with Graphs
Stanford / Fall 2021 - Coggle…
course outline
GCN, GraphSAGE, GAT, Theory of GNNs
- Knowledge graphs and reasoning
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- Methods for generating node embeddings
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- Deep generative models for graphs
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- Applications in Biomedicine, Science and Industry
recommender system, fraud detection etc.
19 topics
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- Traditional Methods for ML for Graphs
- Label Propagation for Node Classification
- Introduction to ML for Graphs
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- Graph Neural Networks 1: GNN Model
- Graph Neural Networks 2: Design Space
- Applications of Graph Neural Networks
- Theory of Graph Neural Networks
- Knowledge Graph Embeddings
- Reasoning over Knowledge Graphs
- Frequent Subgraph Mining with GNNs
- Community Structure Networks
- Traditional Generative Models for Graphs
- Deep Generative Models for Graphs
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