|
|
- ---
- layout: post
- title: Contents
- ---
- <span class="newthought">These notes</span> form a concise introductory course on machine learning with large-scale graphs. They mirror the topics topics covered by Stanford [CS224W](https://cs224w.stanford.edu), and are written by the CS 224W TAs.
- {% include marginnote.html id='mn-construction' note='The notes are still under construction! They will be written up as lectures continue to progress. If you find any typos, please let us know, or submit a pull request with your fixes to our [GitHub repository](https://github.com/snap-stanford/cs224w-notes).'%}
-
- You too may help make these notes better by submitting your improvements to us via [GitHub](https://github.com/snap-stanford/cs224w-notes). Note that submitting substantial improvements will result in *bonus points* being added to your overall grade!
-
- Starting with the Fall 2019 offering of CS 224W, the course covers three broad topic areas for understanding and effectively learning representations from large-scale networks: preliminaries, network methods, and machine learning with networks. Subtopics within each area correspond to individual lecture topics.
-
- ## Preliminaries
-
- 1. [Introduction and Graph Structure](preliminaries/introduction-graph-structure): Basic background for graph structure and representation
- 2. [Measuring Networks and Random Graphs](preliminaries/measuring-networks-random-graphs): Network properties, random graphs, and small-world networks
- 3. [Motifs and Graphlets](preliminaries/motifs-and-structral-roles_lecture): Motifs, graphlets, orbits, ESU
-
- ## Network Methods
-
- 1. [Structural Roles in Networks](): RolX, Granovetter, the Louvain algorithm
- 2. [Spectral Clustering](network-methods/spectral-clustering): Graph partitions and cuts, the Laplacian, and motif clustering
- 3. [Influence Maximization](network-methods/influence-maximization): Influential sets, submodularity, hill climbing
- 4. [Outbreak Detection](network-methods/outbreak-detection): CELF, lazy hill climbing
- 5. [Link Analysis](network-methods/pagerank): PageRank and SimRank
- 6. [Network Effects and Cascading Behavior](network-methods/network-effects-and-cascading-behavior): Decision-based diffusion, probabilistic contagion, SEIZ
- 7. [Network Robustness](): Power laws, preferential attachment
- 8. [Network Evolution](): Densification, forest fire, temporal networks with PageRank
- 9. [Knowledge Graphs and Metapaths](): Metapaths, reasoning and completion of KGs
-
-
- ## Machine Learning with Networks
-
- 1. [Message Passing and Node Classification](): Label propagation and collective classification
- 2. [Node Representation Learning](machine-learning-with-networks/node-representation-learning): Shallow, DeepWalk, TransE, node2vec
- 3. [Graph Neural Networks](machine-learning-with-networks/graph-neural-networks): GCN, SAGE, GAT
- 4. [Generative Models for Graphs](machine-learning-with-networks/graph-generative-models): GraphRNN
|
