Reciprocity and Large Degree Dependence in Random Networks


10:00 AM - 11:00 AM



  • Time: 10:00-11:00AM
  • Date: Tuesday, November 28, 2023
  • Venue: MB441


Users of social networks display diversified behavior and online habits. For instance, a user's tendency to reply to a post can depend on both the user and the person posting. For convenience, we group users into aggregated behavioral patterns, focusing here on the tendency to reply or reciprocate to a message. The reciprocity feature in social networks reflects the information exchange among users. We study properties of a preferential attachment model with heterogeneous reciprocity levels, and give the growth rate of model edge counts as well as prove convergence of empirical degree frequencies to a limiting distribution. This limiting distribution is not only multivariate regularly varying, but also has the property of hidden regular variation.


Tiandong Wang is a Young Investigator at Shanghai Center for Mathematical Sciences (SCMS), Fudan University since September 2022. Before joining Fudan, she was an Assistant Professor in the Department of Statistics at Texas A&M University (09/2019–08/2022).

Her research interest lies on the interface of applied probability and statistics, especially the modeling of heavy-tailed phenomena in complex networks. She received her Ph.D. in Operations Research from the School of Operations Research and Information Engineering at Cornell University in August 2019, under the supervision of Prof. Sidney Resnick.

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