cs224w-notes-and-works

cs224w notes, translated to Chinese and keep the English original version from stanford-cs224w-notes

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已翻译部分

Preliminaries

Introduction and Graph Structure: Basic background for graph structure and representation

Measuring Networks and Random Graphs: Network properties, random graphs, and small-world networks

Motifs and Graphlets: Motifs, graphlets, orbits, ESU

Network Methods

Structural Roles in Networks: RolX, Granovetter, the Louvain algorithm

Spectral Clustering: Graph partitions and cuts, the Laplacian, and motif clustering

Influence Maximization: Influential sets, submodularity, hill climbing

Outbreak Detection: CELF, lazy hill climbing

Link Analysis: PageRank and SimRank

Network Effects and Cascading Behavior: Decision-based diffusion, probabilistic contagion, SEIZ

未翻译部分

Machine Learning with Networks

Message Passing and Node Classification: Label propagation and collective classification

Node Representation Learning: Shallow, DeepWalk, TransE, node2vec

Graph Neural Networks: GCN, SAGE, GAT

Generative Models for Graphs: GraphRNN

cmd23333/cs224w-notes-and-works

cs224w-notes-and-works