Asymptotic Dependence of In- and Out-Degrees in a Preferential Attachment Model with Reciprocity
Oct 26, 2021 09:00 AM Singapore (Registration will open at 08:50 AM.)
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Meeting ID: 966 7974 1725
Reciprocity characterizes the information exchange between users in a network, and some empirical studies have revealed that social networks have a high proportion of reciprocal edges. Classical directed preferential attachment (PA) models, though generating scale-free networks, may give networks with low reciprocity. This points out one potential problem of fitting a classical PA model to a given network dataset with high reciprocity, and indicates alternative models need to be considered. We give one possible modification of the classical PA model by including another parameter which controls the probability of adding a reciprocated edge at each step. Asymptotic analyses suggest that large in- and out-degrees become fully dependent in this modified model, as a result of the additional reciprocated edges.
The paper can be found at: https://arxiv.org/abs/2108.03278
About the Speaker
Tiandong Wang is an Assistant Professor in the Department of Statistics at Texas A&M University (since September 2019). Her research interest is 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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