December 5, 2014 315 Stinson Remick 11:00 am
Dr. Tijana Milenkovic
Dr. Nitesh Chawla Dr. Sidney D'Mello Dr. Aaron Striegel
Students/Faculty are welcome to attend the presentation portion of the defense
"Novel Strategies for Analyzing Structure and Dynamics of Complex Networks and Their 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, since it has become easier to record system evolution, more of dynamic network data is becoming available. 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 the Ph.D. dissertation proposal. 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 and superior 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. Further, we aim to significantly extend the resulting dynamic analysis framework with new methods as well as new applications. Finally, since all of the above discussion dealt only with global or local network structural levels, and since there is rich structural information in between the two structural extremes, we aim to study evolving networks from the intermediate structural level by developing a novel method for dynamic community detection."