Sampling and Inference in Network Analysis

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The study of complex networks constitutes an interdisciplinary area of inquiry that transcends traditional knowledge domains by focusing on the fundamental interdependencies of components within various systems-of-interest. Examples abound from social networks to coupled human and natural systems, from financial networks to disease systems, and from telecommunication networks to energy and power systems. It is the interconnection among these components that often sit at the heart of our most vexing global grand challenge problems, including climate change, energy demands, security, health and wellness, and livelihood and poverty. The study of such complex systems and often large scale networks -- understanding their intrinsic properties, changes to their structure over time or due to external factors, multi-scale behavior of individuals to coarser grained modular communities -- can afford important insights to individuals, organizations and society at large when tackling such grand challenge problems. This project seeks to develop robust and scalable sampling methods for the modeling and analysis of large, potentially dynamic, networks. Sampling is often touted as a means to efficiently combat the inherent complexity of estimating the relevant characteristics of a population. Sampling a network is complicated because they are composed of two units (nodes and edges) that are not always nicely nested. A key objective will be to study and provide a sound mathematical basis along with high performance tools for both node-centric and edge-centric sampling methodologies for the analysis and modeling of networks. The objective of realizing high performance tools for real world applications, drawn from social networks and network biology, will be equally significant, and is necessary for sustained innovation of an inter-disciplinary nature. This research will shed light on the theoretical underpinnings of graph sampling and probabilistic inference in both the static and dynamic network contexts. From an educational standpoint, the investigators will train the next generation of graduate students in this interdisciplinary arena and will also actively encourage participation of undergraduates and under-represented minorities.