The study of network/graph based data has a very long history. The past ten years or so have seen an explosion in the number of contexts and the size of data sets of this kind.Many interesting new statistical problems have appeared. Students in this class will read and present papers on these topics.

## Example topics

Each of these topics has a literature worth surveying and each is undergoing rapid development. Student may also try 'stalker-mode' research where they read, survey and extend the key papers of a prominent networks researcher or research group.

- Finding and testing communities in graphs.
- Studying time trends in graphs that evolve.
- Labeling nodes of a graph based on a small sample of labels.
- Predicting edges that will appear or disappear.
- Models for random graphs.
- Consequences of graph sampling methods.
- Surveying and curating graph data set repositories.
- Surveying and curating R/Matlab/python/etc code for handling and displaying graphs.
There is no conflict in taking this course and other network related courses such as Professor Montanari's Stat 375. The only constraint is that the work done for this course must not duplicate work done for another.

- Art Owen
- Sequoia Hall 130
- My userid is owenbuzzard on stanfordbuzzard.edu (remember to remove the carrion eaters)
- Office hour: Tuesday 11:00-12:00

Monday January 3 1:15 Fishbowl. Regular meetings Tuesdays 1:15 420-050. We will not meet Tuesday January 4.

- Patrick Perry's network science reading list
- Jure Leskovec's publications page
- Winter Mason's web page
- Eric Xing's publications page
- David Gleich's presentations page
- Also ... search for Kleinberg, Watts, M.E.J. Newman, Kolaczyk on the web

Date | Speaker | Topic |

Jan 18 | Michael Koenig | Econometric time series of graphs |

Myunghwan Kim | Kronecker random graphs | |

Rense Corten | A large social data network | |

Jan 25 | Sumit Mukherjee | Exponential familes of graphs |

Hao Chen | Communities and more | |

Sarah Koo | Repositories of graph data | |

Feb 1 | Alexandra Chouldechova | Link prediction | see figures 1,2,3,5,8,9,11 |

Michael Lim | Information diffusion through graphs | |

Feb 8 | Luo Lu | Snijders paper and MCMC |

Sarah Koo | Software for graph computations | |

Feb 15 | Myunghwan Kim | Fitting models like MAGFit to networks |

Feb 22 | Hao Chen | Community detection in protein networks |

Alexandra Chouldechova | Link prediction in relational data | |

Mar 1 | Michael Lim | Detecting emerging trends |

Sumit Mukherjee | Exponential random graph models | |

Mar 8 | Luo Lu | Large deviations for Erdos-Renyi |

Evaluation is based on presentations, a write up, and class participation.