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Abstract:
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Networks have permeated most aspects of our life through everyday realities like the Internet, social networks, and viral marketing. Their use has become especially prevalent in the biological and life sciences, particularly in computational biology and neuroscience. Accordingly, network analysis is an important growth area in the quantitative sciences. As measurement and analysis are integral components of network research, statistical methods play a critical role in network analysis. While network analysis itself is not new -- having exploded onto the scientific scene at least 15 years ago, with roots in social network analysis going back decades and graph theory going back centuries -- its interface with statistics arguably now represents one of the most active and exciting frontiers of our field. Somewhat ironically, however, much of the work being done at this frontier is foundational in nature, entailing fundamental work in topics like modeling, sampling, and design. In this talk I will provide a brief overview of the statistical analysis of network data. Additionally, I will use the context of network sampling to illustrate some of the many opportunities in this area.
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