Graph
1.[Stanford CS224W] Lecture1
Lecture 1.1 - Why Graphs Graphs : a general language for describing and analyzing entities with relations/interactions 많은 type의 graph가 존재한다 ; event graphs, computer networks, disease pathways, social...
2.[Stanford CS224W] Lecture2
Design features for nodes/links/graphs
3.[Stanford CS224W] Lecture3
Node Embeddings Input Graph → Sturucture Features → Learning Algorithm → Prediction by "Representiation Learning" (feature eng.를 대체하도록) Graph Representation Learning Goal : Efficient task-independent ...
4.[Stanford CS224W] Lecture 4
We investigate graph analysis and learning from a matrix perspective.
5.[Stanford CS224W] Lecture 5
Main Question: Given a network with labels on some nodes, how do we assign labels to all other nodes in the network?
6.[Stanford CS224W] Advanced Topics in GNNs
Limitations of Graph Neural Networks A "Perfect" GNN Model A k-layer GNN embeds a node based on the K-hop neighborhood structure A perfect GNN should bhild an injective function b/w neighborhood struc...
7.[Stanford CS224W] Geometric Graph Learning
Previous Lectures Message Passing Neural Nets Node features are updated from iteration t to t+1 via learnable permutation invariant neighborhood aggregate AGG and update UPD Graph Neural Networks Mess...
8.[Stanford CS224W] Trustworthy Graph AI
추천 readingsLIME (Local Interpretation)SHAP (attribution)GNNExplainerGNN Explainability TaxonomyTrustworthy Graph Neural NetworksGraphFramEx Evaluation