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PhD Defense - Lei Meng

Start: 3/18/2016 at 1:00PM
End: 3/18/2016 at 4:30PM
Location: 258 Fitzpatrick Hall
Attendees: Faculty and students are welcome to attend the presentation portion of the defense. Light refreshments will be served.
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Lei Meng

Dissertation Defense

March 18, 2016   at    1:00 pm

  258 Fitzpatrick Hall


Dr. Tijana Milenkovic and Dr. Aaron Striegel

Committee Members:

Dr. Nitesh Chawla          Dr. David Hachen          Dr. Gregory Madey


Computational Strategies for Analyzing Dynamic and Heterogeneous Networks and Their Interdisciplinary Implications


Networks have been used to model a variety of real-world phenomena in many domains. Due to limitations of techniques for data collection, traditional network research has typically focused on studying static and homogenous networks. However, many interactions (e.g., social communications or relationships between biomolecules) are evolving and vary in type. With the recent advancement of data collection techniques, increasing amounts of dynamic and heterogeneous network data are becoming available. Extracting knowledge from such data is a non-trivial task due to the lack of methods for their analyses and consequently many challenging questions have emerged both on the computational as well as the application side.

Therefore, this Ph.D. dissertation focuses on developing computational strategies for analyzing dynamic and heterogeneous networks and studying their interdisciplinary implications. Here, we explore the domains of social and biological networks, although the strategies are applicable to other domains as well. In particular, we are interested in three key questions: 1) How does heterogeneous network data impact network findings using traditional analyses? 2) How to systematically analyze network data that is both dynamic and heterogeneous? 3) How to efficiently compare two heterogeneous yet related networks via network alignment?

To this end, we: 1) integrate heterogeneous network data and demonstrate that our approach reveals additional information that is missed by simpler approach–homogenous network analysis, by exploring a smartphone study encompassing multiple link types and node traits; 2) introduce a novel computational framework for systematic analysis of dynamic and heterogeneous networks, which we use to link individuals’ evolving social network positions with their traits, revealing in the process additional links that are missed by simpler approaches such as static network analysis or that have not been studied to date; and 3) introduce the first ever comparison of two complementary types of network alignment methods (local and global) and propose a new algorithm, IGLOO (Integrating and reconciling Global and LOcal biological network alignment), to integrate and reconcile the two, demonstrating in the process the superiority of IGLOO over each network alignment type individually.