Understanding complex networks using graph spectrum

Complex networks are becoming indispensable parts of our lives. The Internet, wireless (cellular) networks, online social networks, and transportation networks are examples of some well-known complex networks around us. These networks generate an immense range of big data: weblogs, social media, the Internet traffic, which have increasingly drawn attentions from the computer science research community to explore and investigate the fundamental properties of, and improve the user experiences on, these complex networks. This work focuses on understanding complex networks based on the graph spectrum, namely, developing and applying spectral graph theories and models for understanding and employing versatile and oblivious network information - asymmetrical characteristics of the wireless transmission channels, multiplex social relations, e.g., trust and distrust relations, etc - in solving various application problems, such as estimating transmission cost in wireless networks, Internet traffic engineering, and social influence analysis in social networks.