Deconvolution of Networks into Communities

author: Jure Leskovec, Computer Science Department, Stanford University
published: Sept. 27, 2013,   recorded: August 2013,   views: 4938


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Activity of millions of humans on the Web leaves massive digital traces, that can be naturally represented and analyzed as complex dynamic networks of human interactions. Today the Web is a 'sensor' that captures the pulse of humanity and allows us to observe phenomena that were once essentially invisible to us: the social interactions and collective behavior of hundreds of millions of people. In this talk we discuss how large-scale data analytics can be applied to model user behavior in online networks and to inform the design of future online computing applications: How will a community or a social network evolve in the future? How friends in the network shape one's opinions? How can we create incentives to influence the evolution of an online community? We discuss algorithmic methods that scale to massive networks and mathematical models that seek to abstract some of the underlying phenomena.

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