A large number of biological, physical, and social systems contain complex networks. Knowledge about how these networks operate is critical for advancing a more general understanding of network behavior. To this end, each of these disciplines has created different kinds of statistical theory for inference on network data. To help stimulate further progress in the field of statistical inference on network data, the NRC sponsored a workshop that brought together researchers who are dealing with network data in different contexts. This book - which is available on CD only - contains the text of the 18 workshop presentations. The presentations focused on five major areas of research: network models, dynamic networks, data and measurement on networks, robustness and fragility of networks, and visualization and scalability of networks.
- Frontmatter
- Preface and Workshop Rationale
- Contents
- Keynote Address, Day 1 Network Complexity and Robustness--John Doyle, California Institute of Technology
- Neurons, Networks, and Noise: An Introduction--Nancy Kopell, Boston University
- Mixing Patterns and Community Structure in Networks--Mark Newman, University of Michigan and Santa Fe Institute
- Dynamic Networks--Embedded Networked Sensing (Redux?)--Deborah Estrin, University of California at Los Angeles
- Dynamic Network Analysis in Counterterrorism Research--Kathleen Carley, Carnegie Mellon University
- Data and Measurement--Current Developments in a Cortically Controlled Brain-Machine Interface--Nicho Hatsopoulos, University of Chicago
- Some Implications of Path-Based Sampling on the Internet--Eric D. Kolaczyk, Boston University
- Network Data and Models--Martina Morris, University of Washington
- The State of the Art in Social Network Analysis--Stephen P. Borgatti, Boston College
- Keynote Address, Day 2--Variability, Homeostasis per Contents and Compensation in Rhythmic Motor Networks--Eve Marder, Brandeis University
- Dynamics and Resilience of Blood Flow in Cortical Microvessels--David Kleinfeld, University of California at San Diego
- Robustness and Fragility--Jean M. Carlson, University of California at Santa Barbara
- Stability and Degeneracy of Network Models--Mark S. Handcock, University of Washington
- Visualization and Scalability--Characterizing Brain Networks with Granger Causality--Mingzhou Ding, University of Florida
- Visualization and Variation: Tracking Complex Networks Across Time and Space--Jon Kleinberg, Cornell University
- Dependency Networks for Relational Data--David Jensen, University of Massachusetts
- Appendix A Workshop Agenda and List of Attendees
- Appendix B Biographical Sketches of Workshop Speakers
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