Network & Link Analysis

Network & Link Analysis (2 days)

This course goes beyond the traditional supervised and unsupervised learning techniques to identify patterns in your business data. Network Analysis focuses on relationships between or among entities, rather than basing models on static individual profiles. It is used widely in the social and behavioral sciences, as well as in political science, economics, organizational studies, behavioral biology, and industrial engineering. In this course you will learn about the structure and evolution of networks, drawing on knowledge from disciplines as sociology, mathematics, computer science, economics, and physics. We will give an introduction to graph theory and the use of directed graphs to study actor interrelations, present how do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? Based on hands-on analysis of real-world data sets we will demonstrate different fields of applications: from identifying important nodes in the network, to detecting community clusters, to tracing information diffusion and opinion formation.

  • Introduction to graph theory
  • Network concepts (nodes, edges, adjacency matrix, one ore two-mode networks, node degree)
  • Network centrality
  • Network communities
  • Metrics for analyzing networks
  • Network optimization
  • Link prediction
  • Applications of Network Analysis

Who should attend

Data Scientists, statisticians, mathematicians, business analysts, market researchers, risk and fraud analysts, computer scientist and information technology professionals who need to get started with network & link analysis and want to make better use of their data.

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