Project presentations: Tuesday, April 18 at class (1:25pm - 2:40pm) AND at lab (6:15pm - 7:30pm). Attendance mandatory.

Deadline for paper submissions: May 2 by email.

This project is meant to demonstrate some of the things you have learned in the class applied to real world data or theory problems. The project is rather open ended but must come from one of the following themes:

  1. Replication of an existing network paper: This type of project requires replicating (that is writing your own code!) someone else’s work, testing how well it works and maybe trying to extend it a little bit.
  2. Data analysis applying tools from class: This type of project requires a desire to do some novel data analysis. You should find your own network dataset and explore it — the goal here is to do more than just count the number of edges and triangles or to fit a stochastic blockmodel but rather to find interesting insights into some real world phenomenon.
  3. Original research with a network component: If your research already involves networks, be it theoretical, methodological and data-analytic, present it in a compelling fashion.

You can either work by yourself or with one partner. Results to be submitted as a report and a presentation (depending on how many groups we have we should be able to get everyone to do 10-15 minute presentations).

Project presentations:

  • Haozhang Jiang: Mixed Membership Stochastic Blockmodels
  • Derek Owens-Oas: Visualizing a Political Blog Network with the Mixed Membership Stochastic Block Model
  • Vaishakhi Mayya and Ran Huo: A brief study of “Overlapping Stochastic Block Models with Application to the French Political Blogosphere”
  • Leonardo Shu and Sunith Suresh: Comparing Clustering Techniques for Community Detection
  • Mengke Lian: Community Detection and Recommender System of Bangumi Dataset with Social Regularization
  • Zek Zhang: “Stochastic blockmodel approximation of a graphon: Theory and consistent estimation” by Airoldi, Costa and Chan, 2014
  • Jiancong Zhu: “The Political Blogosphere and the 2004 US Election: Divided They Blog”
  • Marco Morucci: Causal inference and social networks: the problem of interference

  • Woo Min Kim: Hierarchical Network Models
  • Gilad Amitai: “Testing and Modeling Dependencies Between a Network and Nodal Attributes” by Fosdick and Hoff, 2015
  • Yoon Woo Billy Byun: Inferring Biological Neural Connectivity with Structured Network Priors
  • Matt Van Liedekerke and Yizheng Wang: Modeling Dynamic NBA Player Passing Networks
  • Sonia Xu: Passing networks: Duke Women’s Basketball
  • Andy Cooper: Analyzing Risky Behaviors in STI Transmission Networks with Latent Space Models
  • Anna Yanchenko: Network Analysis of Concert Programming
  • Dewen Xu and Yimeng Jia: Graph-Coupled HMMs for Modeling the Spread of Infection