Abstract
Networks are widely used in the biological, physical, and social
sciences as a concise mathematical representation of the topology of systems of
interacting components. Understanding the structure of these networks is
one of the outstanding challenges in the study of complex systems.
Many simple forms of structure in networks have been described and
quantified, but it is possible for networks also to contain more complex
structural features, including patterns and correlations on many different
scales.
This talk will describe some new methods for revealing structural
features of networks in an automated fashion and give examples of their
application to a variety of real-world networks.
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