Extremal properties of evolving networks: local dependence and heavy tails

A network evolution with predicted tail and extremal indices of PageRank and the Max-Linear Model used as node influence indices in random graphs is considered. The tail index shows a heaviness of the distribution tail. The extremal index is a measure of clustering (or local dependence) of the stochastic process. The cluster implies a set of consecutive exceedances of the process over a sufficiently high threshold. Our recent results concerning sums and maxima of non-stationary random length sequences of regularly varying random variables are extended to random graphs. Starting with a set of connected stationary seed communities as a hot spot and ranking them with regard to their tail indices, the tail and extremal indices of new nodes that are appended to the network may be determined. This procedure allows us to predict a temporal network evolution in terms of tail and extremal indices. The extremal index determines limiting distributions of a maximum of the PageRank and the Max-Linear Model of newly attached nodes. The exposition is provided by algorithms and examples. To validate our theoretical results, our simulation and real data study concerning a linear preferential attachment as a tool for network growth are provided.

Automated summary

This article examines the network evolution of random graphs using predicted tail and extremal indices of PageRank and the Max-Linear Model as node influence indices. The tail index indicates the heaviness of the distribution tail, while the extremal index measures clustering or local dependence of the stochastic process. By ranking connected stationary seed communities based on their tail indices, the tail and extremal indices of newly appended nodes can be determined.