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Scalable Algorithms on Large Graphs Based on Sampling

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  • Fri, 05/19/2017 - 12:00pm - 2:00pm




Scalable Algorithms on Large Graphs Based on Sampling

 

PhD. Comprehensive Exam by:

Roohollah Etemadi

Date:   Friday, May 19th, 2017
Time: 12:00 pm
Location: Lambton Tower, 3105

Abstract:Graphs are used to model the interaction among entities in many real networks in computer science, the Internet, biology, chemistry, economic, and many other fields. Metrics such as the number of triangles (∆), clustering coefficient (C), average shortest path length (ASPL), and community structure have been used to understand the complex structure of such graphs. Recently, however, two challenges have arisen. First, computing such properties using traditional methods is expensive in term of time and memory usage on large graphs. Second, direct computing is impossible when the entire data is inaccessible. For instance, user networks in Twitter and Facebook are not available for third parties to explore their properties directly. Therefore, sampling based methods are indispensable.  This presentation will cover basic information on sampling methods and recent progress to estimate ∆, C, and ASPL, and to detect community structure on large graphs using sampling techniques.

Thesis Committee:     
Internal Reader: Dr. Mehdi Kargar and Dr. Dan Wu  
External Reader: Dr. Majid Ahmadi
Advisors: Dr. Jianguo Lu and Dr. Yung H. Tsin             



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