Problems in many science and engineering fields center on the study of large, complex systems consisting of interconnected elements with different attributes and function. Such systems are naturally represented as networks, with a global structure consisting of both the topology of connections among network elements and the distribution of attributes (or functions) inherent in the elements themselves. The power of the network representation lies in its ability to express many apparently different kinds of systems within a common formal framework, allowing for cross-application of computational and analytical techniques across fields. While the promise of such cross-applicability is great, advancement has been hindered by the difficulty of bridging the substantive gulf between different areas of research. The goal of this research is to leverage recent developments in three such areas, namely computer, social, and biological networks, to realize the potential of an integrated, interdisciplinary approach to the study of systems with complex network structure.
Our research focuses specifically on the interaction between network topology and attributes. Central problems we will address include: the modeling of interactions between network topology and element attributes or function; the characterization of unknown network structure from imperfect and incomplete data; and the development of associated algorithms which will scale efficiently to large systems. Computational efficiency is an important dimension of our work, as we are dealing with the measurement and analysis of massive network data sets. We will use the techniques we develop to address important problems in three application domains: computer networks and security (e.g., methods for detection of malicious behavior on the Internet); online social networks (e.g., the reproduction of social stratification in online environments); and biological networks (e.g., biological signatures of disease). The result will be a unified collection of methods, software tools, and data sets that will enable and accelerate development in these research areas.
The intellectual merit of this work lies in the joint analysis of network topology and function in attribute-rich networks across fields. The project will lead to the development of practical techniques and methodologies for data collection and analysis that can be applied to many substantively distinct problems. Dissemination of results will be achieved through research publications, publicly available software and data sets, and communication with relevant practitioner communities. The research will be integrated with curriculum development and student advising and will promote interdisciplinary training of students. The project will promote diversity, not only through the synthesis of the research team, but also through enhancing the understanding of phenomena such as segregation and attitude polarization in online environments.
