Sessions at PyCon US 2012 about Social Network Analysis and Python

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Thursday 8th March 2012

  • Social Network Analysis with Python

    by Maksim Tsvetovat

    Social Network data permeates our world -- yet we often don't know what to do with it. In this tutorial, I will introduce both theory and practice of Social Network Analysis -- gathering, analyzing and visualizing data using Python and other open-source tools. I will walk the attendees through an entire project, from gathering and cleaning data to presenting results.

    SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network). Thus, we may arrive at results that may seem counter-intuitive -- e.g. that Justin Bieber (7.5 mil. followers) and Lady Gaga (7.2 mil. followers) have relatively little actual influence despite their celebrity status -- while a middle-of-the-road blogger with 30K followers is able to generate tweets that "go viral" and result in millions of impressions.

    In this tutorial, we will conduct social network analysis of a real dataset, from gathering and cleaning data to analysis and visualization of results. We will use Python and a set of open-source libraries, including NetworkX, NumPy and Matplotlib.


    • Introduction. Why should we do this? What is the data like? Why is this different from other techniques? What can we learn?
    • Centralities: Degree, closeness, betweenness, PageRank, Klout Score
    • Beyond Klout Score: Finding communities of interest, finding clusters in networks
    • Information diffusion in networks -- how do things go viral?

    At 9:00am to 12:20pm, Thursday 8th March

    In D3, Santa Clara Convention Center

    Coverage video