Introduction to Complex Networks Modeling and Analysis with Python/NetworkX (ICoNMAP)
Binghamton University, State University of New York, USA
The science of complex networks has been increasing its relevance to a wide variety of scientific disciplines over the last decade, including biology, physics, computer science, cognitive science, social sciences, engineering, and many others. It offers concepts and tools to help us understand various complex systems in nature from a viewpoint of connection and interaction. Subjects being studied range from the way cells function, to the ways neurons connect and configure brains, to the ways societies and economies grow and behave, to name a few. In such complex network research, computational tools for modeling and analyzing networks play a crucial role.
This tutorial will provide a quick introduction to Python and NetworkX, a very powerful computational toolkit for modeling, analyzing and simulating complex networks. Specific topics to be introduced will include: Data structure and manipulation in Python/NetworkX, data import and export, network visualization, network analysis (classical graph-theoretic algorithms, shortest paths, clustering, degree distribution, assortativity, centrality, core/community detection, etc.), generative network models, and dynamic simulations of complex networks. Instructions will be given through many hands-on activities, so participants should bring their own laptops. Prior knowledge of Python is helpful, but not strictly required.
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Questions? Email Hiroki Sayama