Statistical analysis of complex networks using NetworkX with applications

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Abstract
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Networks can be used to model many types of relations and processes in physical, biological, social and information systems. Many practical problems can be represented by networks, also called graphs. This tutorial will first go over the basic building blocks of graphs (nodes, edges, paths, etc) and common methods to get data structures into NetworkX-compatible format. Then, we can solve common problems like shortest-path on a real graph, and less common ones, like implementing PageRank on a directed network to get PageRank of each node in the network.

Bio
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Lisa is a Data Scientist at Lagrangian.io, a startup focused on the use of Terraform modules for backend services optimization. Previously, she was a bioinformatician with Geisinger Health System working on large-scale genomics applications related to human health outcomes. She earned her MS in Bioinformatics at SoongSil University in Korea where she worked on chemical analysis methods for simulated chemical structure (QSAR/QSPR) in Python.