Speaker
Sang Hoon Lee
(Complex Systems and Statistical Physics Lab, KAIST)
Description
Recently, massive digital records have made it possible to
analyze a huge amount of data in social sciences such as
social network theory. We investigate social networks
between people by extracting information on the World Wide
Web. Using famous search engines such as Google, we quantify
relatedness between two people as the number of Web pages
including both of their names and construct weighted social
relatedness networks. The weight and strength distributions
are found to be quite broad. A class of measure called the
R{\'e}nyi disparity, characterizing the homogeneity of
weight distribution for each node, is presented. We
introduce the maximum relatedness subnetwork, which extracts
the most essential relation for each individual. We analyze
the members of the 109th United States Senate as an example
and demonstrate that the methods of construction and
analysis are applicable to various other social groups and
weighted networks.
In addition, I will present some results of my recent works on phase transition in annealed scale-free networks, etc.
In addition, I will present some results of my recent works on phase transition in annealed scale-free networks, etc.
