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

该项目的目标是深入了解拥有数百万甚至数十亿用户的社交网络服务(SNS)的性质、基本模型和动态。正在设计使用信号处理和信息论技术分析海量SNS图表的方法，以回答有关SNS的基本问题。其目标是开发新的方法来(I)识别SNS网络中的有影响力的节点，并了解这些节点是如何随着时间的推移而演化和交互的，(Ii)从SNS图中推断人物S的社交图，以及(Iii)通过分析另一个SNS图来推断SNS网络的特性。为了实现这些目标，信号处理和信息/图论方法被用于：(A)将海量SNS图中包含的信息投影到紧凑的表示上；以及(B)理解不同的图(例如，表示社交网络服务的不同时间实例或表示不同的服务)如何相互关联。更深入地了解社交网络的结构以及这种结构是如何演变的，可以应用于各种社会问题。该项目本质上是跨学科的，它连接了几个不同的社区，包括电气工程、计算机科学和社会科学；它促进了它们之间的互动和交流。为了促进教育和学习，该项目积极吸引高中生、本科生和研究生，特别是来自代表性不足的少数民族的学生。

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.