Social Networking and Social Media: Related Funding, Research Overview, Selected Papers

    Related Funding:

  1. CMMI 1436786: Collaboratve Research: Coordinated Real-Time Trafc Management based on Dynamic Informaton Propagation and Aggregaton under Connected Vehicle Systems, a three-year grant from the National Science Foundation. Lili Du, and Xiang-Yang Li, $240,000. In this project we study how information can be collected and will be propagated in a large scale vehicular networks. We also investigate how user privacy is protected when participated in this process.
  2. Social Network Dynamics: Belief and Information Propagation, PIs: Jennifer Miller, Xiang-Yang Li, Saniv Kapoor, IIT ERIF, 2013.1-2013.12, $20,000.
  3. HK RGC: Explore Business Models for Streaming Applications in Peer-to-Peer Environments. Wei Lou, and Xiang-Yang Li. CERG under Grant PolyU-5232/07E. 01-01-2008 to 31-12-2009, HK$ 378,400.
  4. HK RGC: A Microeconomic Approach for Digital Rights Management in P2P Networks, XiaoWen Chu, and Xiang-Yang Li. RGC HKBU 210406, from 01-09-2006 to 28-02-2009, HK$356,000.

    Course Created and Taught at IIT:

  1. CS495/595: Social Networking: Theory and Applications
  2. IPRO 316: Belief Propagation in Social Networks 2012 Fall semester. Co-taught with Jennifer Miller and Sanjiv Kapoor. This is for undergraduate student project

    Research Overview:

    Online social networks are fast becoming an important communication medium amongst varied groups of people. With the advent of popular web-sites and communication tools (e.g., Facebook, Twitter), users of these sites and tools form large social networks that provide a powerful means for sharing, organizing and finding contents and contacts. Other interesting applications include political activity and political activism which have been harnessing the powers of digital social media. In this context, online social networking is a very powerful tool for many reasons. First, the broadcast nature of some social networking sites enables individuals to access a large audience, and second the network can also be used to rapidly spread the influence on others.
    Our research in social networking focuses on several different areas.
    First, we investigate the power of social networks to influence beliefs as well as belief propagation and adoption by analyzing some fundamental research issues in social networking. Studying and utilizing various characteristics of the social network structures and understanding the impact on information propagation have been recently studied by computer scientists, and sociologists and psychologists. These are more often separate endeavors: computer scientists mainly focused on the network structures and their impact while ignoring the belief formation and reinforcement of knowledge; on the other hand, psychologists typically focused on the cognitive process of social learning by individuals, while the phenomenon of information propagation within social networks has received relatively little attention. This project will treat the information propagation and social networks as an integrated dynamic process: information propagation helps to enrich the social network structures, and social networks enable fast and efficient information propagation, learning, and adoption.
    Second, we study two tightly coupled topics in online social networks (OSN): relationship classification and information propagation. The links in a social network often reflect social relationships among users. In this work, we first investigate identifying the relationships among social network users based on certain social network property and limited pre-known information. Social networks have been widely used for online marketing. A critical step is the propagation maximization by choosing a small set of seeds for marketing. Based on the social relationships learned in the first step, we show how to exploit these relationships to maximize the marketing efficacy. We evaluate our approach on large scale real-world data from Renren network, confirming that the performances of our relationship classification and propagation maximization algorithm are pretty good in practice.
    Third, we design privacy preserving protocols for mobile social networking applications, including friend-matching, and location based services. In our designed protocols, we show that no one could get the exact information about the location publisher except the publisher himself and the ones who he allowed, even the LBS service provider does not know the information. Four different levels of information protection are introduced to the queries.
    Fouth, we design mobile devices that will facilitate the networking building for mobile social networking. Specifically, we designed iGaze glass by which two users can build social connection by simply gazing at each other.
    Fifth, we also study the asymptotical behavior of large scale social networks, such as the capacity of mobile social networks, the diameter of social networks when some random choices are introduced. Li’s research is contributing and is supported in part by the National Science Foundation, through CMMI and IIT ERIF program.