Oral Candidacy - Yuxiao Dong
|Start:||1/22/2016 at 10:00AM|
|End:||1/22/2016 at 12:00PM|
|Location:||384H Nieuwland Hall|
Faculty and Students are welcome to attend the presentation portion of the exam.
Oral Candidacy Exam
January 22, 2016 10:00 am 384H Nieuwland Hall
Adviser: Dr. Nitesh Chawla
Dr. David Chiang Dr. Omar Lizardo Dr. Zoltan Toroczkai
Computational Lens on Large Social Networks:
Phenomena, Models and Applications
This proposal aims to apply computational lens on large social systems to unveil the social phenomena emerged from individual users’ interactions, model and predict user social behavior, and ultimately translate the research into real-world applications. The planetary-scale user base from online social platforms (e.g., Facebook and Twitter) and offline communication networks (e.g., phone call and text messaging) offers the source of social network research. Work in this proposal investigates the various ways that diverse individuals are embedded in and interact within these social networks, and reasons out the underlying principles that drive individuals’ social behavior. Work on sociodemographic modeling leads to the discovery of gender- and age-based social strategies that are used to satisfy human social needs, and introduces a distributed graphical model that demonstrates an 80% predictability of user demographic profiles from social networks. Work on modeling link formation in coupled networks will present a computational framework that is able to uncover one network’s structure by using another network and the interactions between these two networks. Work on modeling social capital finds that in academic collaboration networks, the researcher’s authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the author’s h-index, while the topic popularity and the co-authors’ h-indices are of surprisingly little relevance, and also finds a greater than 87.5% potential predictability for whether a paper will contribute to an author’s h-index within five years. Proposed work on cultural effects on social behavior will provide us with a better understanding of the diversity of our society, and with concrete suggestions for supporting and embracing a diversely connected world. To this end, modeling social behavior in digital environments offers the potential to understand the fundamental principles that drive our social decisions and activities—from individuals, to cultures, to societies—and, in this way, to help us better understand ourselves and each other.