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Title:

Communities, Modules, and Large-Scale Structure in Networks

Description: Many networks, including social and technological networks, are found to divide into communities or modules, groups of network nodes with dense connections within groups and sparser connections between groups. The ability to detect and identify communities plays an important role in network visualization, data analysis, and other areas. In this talk I discuss the theory and practice of community detection and demonstrate applications to networks from a range of fields. I also discuss some recent results showing that in certain cases it is impossible to detect communities in networks, even though they are present, implying that there are fundamental limits to our ability to pry understanding from network data. Finally I discuss other forms of large-scale structure in networks, such as hierarchical structure and overlapping communities, and methods for their detection. These studies are in their infancy but may lead us to new understanding of the connection between the structure and function of networked systems.

Mark Newman received a Ph.D. in theoretical physics from the University of Oxford in 1991 and conducted postdoctoral research at Cornell University before joining the staff of the Santa Fe Institute, a think-tank in New Mexico devoted to the study of complex systems. In 2002 he left Santa Fe for the University of Michigan, where he is currently the Paul Dirac Collegiate Professor of Physics and a professor in the university’s Center for the Study of Complex Systems. Professor Newman is a Fellow of the American Physical Society and the author of over a hundred scientific publications and six books, including “Networks: An Introduction”, a textbook on network theory, and “The Atlas of the Real World,” a popular book on cartography. Professor Newman’s research focuses on networked systems, such as social and information networks, and particularly on questions of community structure, network resilience, mixing patterns, and statistical inference for networks.

Date Issued:

2012-05-18

Time:

2:25 PM

Url:

http://lecb.physics.lsa.umich.edu/CWIS/browser.php?ResourceId=4313

CreatorInstitution:

University of Michigan

Duration:

00:40:52

Creator:

Newman, Mark