What do COVID, fraud, Facebook, and fake news have in common? They are all networks! And failure to recognize network properties will result an incomplete analysis. For example, why do some people spread COVID more than others? In terms of finding another job, is it more effective to depend on a few great friends, or is it better to have 100 acquaintances?
Network analysis is an analysis methodology where relationships and items are arranged by their relative position within the complete network. As such, it provides a unique lens for which there is no other proxy.
This presentation will introduce the audience to the principles of network analysis, both in terms of the graphical layout, and the resulting statistics like eigenvector centrality, nodes-out, authority score, and others. The presentation will conclude with several use-cases demonstrating the unique power of network analysis
Amit is an environmental engineer turned data director and now a freelance consultant. He started his career collecting and using environmental data, then disseminated SDG/MDG data with UN-FAO, then led data initiatives at an NGO, and now brings success to multiple clients. Passionate about #data4good, network analysis, and quantifying weird qual data.
He is passionate about R and is dedicating to promoting its usage having led R-User groups in Ghana and the UK. He is the author of the ShinyTester, TileMaker, and BulletChartR R packages and blogs at https://www.amitkohli.com.