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Name: Tiandong Wang

Talk Title: Reciprocity and Large Degree Dependence in a Preferential Attachment Model

Abstract: Empirical studies show that online social networks have not only in- and out-degree distributions with Pareto-like tails but also a high proportion of reciprocal edges. A classical directed preferential attachment (PA) model generates in- and out-degree distribution with power-law tails, but theoretical properties of the reciprocity feature in this model have not yet been studied. We derive the asymptotic results on the number of reciprocal edges between two fixed nodes, as well as the proportion of reciprocal edges in the entire PA network. We see that with certain choices of parameters, the proportion of reciprocal edges in a directed PA network is close to 0, which differs from the empirical observation. 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 then discuss one possibility and study the dependence structure between large in- and out-degrees.

This talk is a contributed talk at EVA 2021.