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Scalable Algorithms on Large Graphs

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  • Fri, 01/12/2018 - 12:00pm - 2:00pm

Scalable Algorithms on Large Graphs Based on Sampling

PhD. Thesis Proposal by:

Roohollah Etemadi

Date:   Friday, January 12, 2018
Time: 12:00 pm - 2:00 pm
Location: 3105, Lambton Tower

Abstract: Graphs are ubiquitous. They model the interactions among entities in many real networks in computer science, the Internet, biology, chemistry, economics, and many other fields.  Analyzing such graphs has gained increasing attention in academia and industry. Structural metrics such as the number of triangles (∆), and clustering coefficient (C) have been used to understand the complex structure of graphs of real networks. Moreover, much work has been directed to reveal their community structure.

In the era of big data, graphs of real networks consist of billions of nodes and edges. Therefore, computing the metrics of such graphs is a computationally intensive task. Also, direct computing is impossible when the entire data is inaccessible. For instance, whole user networks in Twitter and Facebook are not available for third parties to explore their properties directly. As a result, sampling based methods are essential. 

This presentation covers existing methods along with our proposed techniques to estimate ∆, and C. It also summarizes state-of-the-art techniques and our approaches to detect community structure in large graphs.

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

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