veyepar: Intro to Network Science

Author(s):	Christopher Roach
Location	A2
Date	mar Tue 19	Days Raw Files
Start	10:15	First Raw Start	10:30
Duration	00:50:00	Offset	0:15:20
End	11:05	Last Raw End	11:38
Chapters	00:00 0:28:40
Total cuts_time	35 min.

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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.

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