Statistical Methods for Modeling Network Distributions

author: Jennifer Neville, Computer Science Department, Purdue University
published: Oct. 12, 2016,   recorded: August 2016,   views: 1232
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

The recent interest in analyzing the network structure of complex systems has fueled a large body of research on both models of network structure and algorithms to automatically discover patterns for use in predictive models. However, robust statistical models, which can accurately represent distributions over graph populations, and sample efficiently from those distributions, are critical to assess the evaluate the performance of analytic algorithms and the significance of discovered patterns. However, unlike metric spaces, the space of graphs exhibits a combinatorial structure that poses significant theoretical and practical challenges to accurate estimation and efficient sampling/inference. In this talk, I will discuss our recent work on modeling distributions of networks, both attributed and unattributed, and outline how the methods can be used for inference and evaluation.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: