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GNN 1.1 Why Graphs
Many Types of Data are Graphs
- Knowledge Graphs
- Regulatory Networks
- Scene Graphs
- Code Graphs
- Molecules
- 3D shapes
Graphs
- Information / Knowledge
- Software
- Similarity networks
- Relational structures
topic
- Traditional methods: Graphlets , Graph Kernels
- Methods for node embeddings : DeepWalk , Node2Vec
- Graph Neural Networks: GCN , GraphSAGE , GAT ,Theory of GNNs
- Knowledge graphs and reasoning : TransE , BetaE
- Deep generative models for graphs
- Applications to Biomedicine , Science , Industry
Applications of Graph ML
Classic Graph ML task
- Node classification
- Link Prediction
- Graph classification
- Clustering
- Graph generation
- Graph evolution
Choice of Graph Representation
Components of a Network
- ObjectS: nodes , vertices N
- Interactions: links , edges E
- System: network , graph G(N,E)
How do you define your graph
Directed vs. Undirected Graphs