|Title: Fairness in Social Networks|
|Seminar: Computer Science|
|Speaker: Sucheta Soundarajan, Syracuse University|
|Contact: Joyce Ho, firstname.lastname@example.org|
|Date: 2021-11-19 at 1:00PM|
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Social networks play a vital role in the spread of information through a population, and individuals in networks make important life decisions on the basis of the information to which they have access. In many cases, it is important to evaluate whether information is spreading fairly to all groups in a network. For instance, are male and female students equally likely to hear about a new scholarship? In this talk, I present the novel "information unfairness" criterion, which measures whether information spreads fairly to all groups in a network. I then discuss the results of a case study on the DBLP computer science co-authorship network with respect to gender, with several surprising results.
Sucheta Soundarajan is an Associate Professor in the Electrical Engineering & Computer Science Department at Syracuse University. Her areas of interest include social network analysis and data mining, and her research covers topics such as network clustering, sampling, information flow, and centrality. She is a recipient of the NSF CAREER award, Army Research Office Young Investigator Award, and the SIAM Science Policy Fellowship. She received her PhD from Cornell University in 2013.
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