The rapidly booming amounts of social networks data from the Internet offers a lot of information to understand human behaviors. First, the networks data contains sparse communication frequencies and some dense clusters, and the clusters change over time, so that feature generation and selection are essential. This research project addresses the statistical challenges for detecting abrupt categories changes in networks. This is important for quantifying human dynamics and accurately identifying unusual events and forecast future threats indicated by those events. Graduate students will be involved in some aspects of the project.

This project aims to develop 1) for the static case: we will use zero-inflated or hurdle models to characterize the class link probability. 2) for the dynamic case: the class communication probability is a variable of time, we model the probability by a self-exciting process. 3) we consider the cold-start problem in which the predicted networks vary a lot from the training network, so that there are no enough samples to train classification models. Instead, we will develop matrix-variate clustering and classification models. This project includes several important topics to improve modeling of the network users' categories and identifying efficiently abrupt network pattern changes in real time as well as reducing the influence of outliers. These methods are applicable to various types of networks data such as social networks, biology signals, genome sequences, and so on. The PIs will provide a publicly-available software packages to implement the proposed methods. Additionally, corresponding statistical theories and computational techniques can be extended to advance further research and can be applied to other fields. This project topics cater to the students with hands-on studies in new Big-Data analysis program at the University of Central Florida.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
Standard Grant (Standard)
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Pawel Hitczenko
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University of Illinois at Chicago
United States
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