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Personalized PageRank based Community Detection

This video was recorded at 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Chicago 2013. Personalized PageRank is a reasonably well known technique to find a community in a network starting from a single node. It works by approximating the stationary distribution of a resetting random-walk and using that stationary distribution to estimate the presence of nearby cuts in the graph. I'll discuss recent work on how to find use a personalized PageRank community to quickly estimate the sets of best conductance anywhere in the graph as well as how to find a good set of seeds to cover the entire graph with personalized PageRank communities.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Programming & Programming Languages

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Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Author:: David F. Gleich, Department of Computer Science, Purdue University
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Accessibility Information Available:: Unknown
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States