The Power of Random Neighbors in Social Networks

The friendship paradox is a sociological phenomenon first discovered by Feld which states that individuals are likely to have fewer friends than their friends do, on average. This phenomenon has become common knowledge, has several interesting applications, and has also been observed in various data sets. In his seminal paper Feld provides an intuitive explanation by showing that in any graph the average degree of edges in the graph is an upper bound on the average degree of nodes. Despite the appeal of this argument, it does not prove the existence of the friendship paradox. In fact, it is easy to construct networks {even with power law degree distributions {where the ratio between the average degree of neighbors and the average degree of nodes is high, but all nodes have the exact same degree as their neighbors. Which models, then, explain the friendship paradox? In this paper we give a strong characterization that provides a formal understanding of the friendship paradox. We show that for any power law graph with exponential parameter in (1,3), when every edge is rewired with constant probability, the friendship paradox holds, i.e. there is an asymptotic gap between the average degree of the sample of polylogarithmic size and the average degree of a random set of its neighbors of equal size. To examine this characterization on real data, we performed several experiments on social network data sets that complement our theoretical analysis. We also discuss the applications of our result to influence maximization.