Intro To Network Science

In 1967 sociologist Stanley Milgram began a series of experiments into the "small world problem" that would firmly cement the phrase "six degrees of separation" within the popular culture. Because of these experiments, nearly all of us today have heard that we are simply a few hand shakes away from anyone in the world. Indeed it's a popular past time amongst academics to figure our their Erdos number and, amongst the rest of us, to calculate a favorite actor's Bacon number. Fast forward to today and the world seems even smaller. With the internet connecting all of us to one another at the speed of light, and social networks such as Twitter and Facebook creating communities that quite literally span the globe, this new era in connectedness has given us a wealth of data about how we interact with one another. There's hardly anyone in the tech community today who hasn't heard of social network analysis, but this combination of sociology, computer science, and mathematics has significance beyond just the analysis of social networks.

Between nearly any set of entities a relationship can be found, and thus a network can be made, from which the inner workings of those relationships can be studied. The still nascent field of network science is quickly becoming THE science of the 21st century and this talk will introduce this budding field and demonstrate how tools such as NetworkX and Matplotlib make it possible for Pythonistas to make meaningful contributions or simply just analyze their own popularity on Twitter.

The goal of this talk is to give the attendees a basic understanding of what network science is and what it can be used for, as well as demonstrate its use in a specific scenario. During the course of this talk we'll walk through a proper definition of a network and introduce some of the jargon necessary to converse with others working in the field. We'll also take a look at some of the statistical properties of networks and how to use them to analyze our own networks. Finally, we'll look at a specific example of the application of network science principles on a real life social network. By the end of the talk, an attendee should feel comfortable enough with field of network science to be able to start analyzing their own networks of data.

Slides can be viewed at: http://www.slideshare.net/PyData/intro-to-network-science