Description
This talk will focus on the scientific analysis of social networks, how to derive useful insights from social graphs using Python, and discuss some lessons learned from taking this analysis out of the lab and into to a useful product in a production setting. I will cover acquiring data, predicting influence or activity, recommending possible friends from the graph and detecting communities within large networks. Experience with tools used to do this, including sklearn, networkx, Spark and Kafka will be covered.