PhD Defense - Yuriy Hulovatyy
|Start:||5/23/2016 at 12:30PM|
|End:||5/23/2016 at 3:30PM|
|Location:||258 Fitzpatrick Hall|
Faculty and students are welcome to attend the presentation portion of the defense. Light refreshments will be served.
May 23, 2016 12:30 pm 258 Fitzpatrick
Adviser: Dr. Tijana Milenkovic
Dr. Chawla, Dr. D'Mello, and Dr. Striegel
Exploring Structure and Dynamics of Complex Networks: Novel Methods and Interdisciplinary Applications
Networks provide a natural and powerful way to model complex real-world systems in various domains. Studying structure of a network can help extract functional knowledge about the corresponding system. As real-world networks exhibit non-trivial organization at many scales, this extraction can be done on different levels: from the global perspective of the whole network to the intermediate perspective of node groups (or communities) to the local perspective of individual nodes. Since the different scales provide different viewpoints on the network structure, each one has its (dis)advantages, depending on the research question of interest.
With new technological advances, the amount of available real-world network data in different domains rapidly increases. In addition to this, networks are growing in size and complexity. For example, whereas traditional network data has been static, because it has become easier to record system evolution, more of dynamic network data is becoming available. Importantly, many network theoretic problems are computationally intractable. For these reasons, it is critical to develop novel computational strategies for efficient extraction of functional information from the structure of such complex (e.g., dynamic) networks. And this is the main focus of this dissertation. We achieve this goal in two different ways, by: 1) answering novel research questions via established network approaches, and 2) developing novel network approaches for established research questions.
In the first context, we apply global network analysis to answer a novel question in a novel domain in which network research has not been used to date -- interpreting affective physiological data, which is critical for the field of affective computing. In addition to this, we employ local network analysis to study the interplay between individuals' social interactions and traits from a new dynamic (rather than traditional static) network viewpoint.
In the second context, we take a well-established local analysis approach for static networks to develop a novel method for a popular computational problem of link prediction. This problem has many real-world applications, one of which -- de-noising biological networks -- is the main motivation of our work. Moreover, we take the same static local approach and develop new theory that allows for dynamic network analysis. We demonstrate that accounting for temporal information helps and that our dynamic analysis framework outperforms the existing static approach. We use our method to study human aging from biological networks. Finally, we introduce a new approach for studying dynamic networks from the intermediate perspective, which deals with the problem of segment community detection. We show that our approach outperforms existing methods in terms of both accuracy and computational complexity.