Скачать или смотреть Use of Python for Complex Network Analysis

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Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into network data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks such as computer networks, technological networks, brain networks and social networks.

Andre Voigt who is a PhD candidate in Eivind Almaas' group at NTNU talks about using Python for analysis of some complex networks one might find in certain scenarios. This is a young field where the potential use cases is growing significantly.