CIF: Small: Modeling and Dynamic Analyzing for Multiplex Social Networks

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The rapid growth of online social networks has made them become one of the most important channels for fast information propagation and influence. This propagation process becomes much more effective when information simultaneously spread on many social sites via overlapping users, who have accounts on multiples social networks sites. Analyzing each network separately will fail to identify the most influential users, and thus give erroneous indicators of information propagation. At present, understanding behavior that crosses multiple networks with multiple layers, dubbed here Multiplex Social Networks, is still largely unexplored. This project aims to develop mathematical models and techniques to precisely and efficiently analyze the information propagation in multiplex networks, capturing the extrinsic interdependencies between networks while preserving each network?s intrinsic properties.At the heart of this project lie novel mathematical techniques to study many dynamical processes in multi-layered multi-dependent networks. This research will lay a foundation in understanding the fundamental properties that contribute to an extremely fast propagation in multiplex social networks, providing a breakthrough in effectively using online social networks and their crowd sourcing capabilities. Furthermore, algorithmic techniques developed in this project are expected to advance research fronts in approximation theory and network science. The findings may also benefit other fields, such as network immunization, offering control of epidemic outbreaks and containment of viruses, and likewise allow us to analyze other dynamic features, such as cascading failures in many modern complex networked systems, which are interdependent. The project will involve students at all levels, with emphasis on attracting students from underrepresented groups via an internship program. The real-world applications will offer an ideal platform to engage undergraduate and K-12 students.This project will comprehensively investigate propagation behaviors in multiplex social networks by pursuing three primary research tasks: 1) modeling a multiplex social network via a new set of graph theory techniques. The model provides a unified framework to extract the interdependencies between networks and the properties of each network in the simplest form; 2) Analyzing the speed of information propagation and identifying the most influential users in the presence of simultaneous spreading via overlapping users. Several new optimization models along with their hardness complexity analyses and approximation algorithms will be investigated; 3) Devising near-optimal solutions for many optimization problems considering the properties of complex networks since many real-world networks, ranging from the Internet to social and biological networks, are complex. This is a novel research direction and success of this approach will impact the design of approximation algorithms for many real-life problems on complex networks.