SoCS: Signal Processing and Information Theoretic Approaches to Denoising and Demystifying Social Network Services

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The objective of this project is to develop in-depth understanding of the nature, underlying models, and dynamics of Social Network Services (SNS) with millions and even billions of users. Methods for the analysis of massive SNS graphs using signal processing and information-theoretic techniques are being designed to answer fundamental questions regarding SNS. The goal is to develop new approaches for (i) identifying influential nodes in SNS networks, and understanding how these nodes evolve and interact over time, (ii) inferring people?s social graphs from SNS graphs, and (iii) inferring properties of an SNS network from analyzing another SNS graph. To achieve these objectives, signal-processing inspired and information/graph theoretic approaches are being employed for: (a) projecting information contained in massive SNS graphs onto compact representations; and (b) understanding how different graphs (e.g., representing different time-instances of a social network service or representing different services) relate to each other.This research enables a spectrum of novel applications that are currently impossible. A deeper understanding of the structure of social networks and how that structure evolves can be applied to a variety of social issues. The project is interdisciplinary in nature and it bridges several different communities including electrical engineering, computer science, and social science; and it fosters interaction and communication among them. To promote education and learning, this project actively engages high school, undergraduate, and graduate students, especially students from under-represented minorities.