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Center for Nonlinear Studies |
Our understanding of the complex structure of real-world networks (both natural and man-made) has improved dramatically in recent years, with much of this newfound knowledge being derived from statistical characterizations of local-level patterns, e.g., the degree distribution. Correspondingly, our models of complex networks tend to focus on these features, and often assume they are homogeneously distributed across the network. In contrast, real-world networks exhibit a strong degree of heterogeneity, which is not captured by these approaches. In this talk, we give a brief critique of the homogeneity assumption for the study of complex networks, and then present a new approach that explicitly captures their heterogeneity. We briefly describe some of the advantages of this approach for future research on complex networks, and describe some practical applications for the near-term.
Host: Hasan Guclu