Parallel Dynamics and Computational Complexity of Network Growth Models

TL;DR

This paper analyzes the parallel computational complexity of network growth models with preferential attachment, revealing a phase transition at alpha=1 where the complexity shifts from logarithmic to constant time.

Contribution

It introduces algorithms for fast parallel generation of preferential attachment networks and uncovers a complexity transition linked to network structure.

Findings

Networks with alpha>1 can be generated in constant time.

For 0<=alpha<1, logarithmic parallel time is needed.

Networks exhibit little depth despite sequential growth rules.

Abstract

The parallel computational complexity or depth of growing network models is investigated. The networks considered are generated by preferential attachment rules where the probability of attaching a new node to an existing node is given by a power, $\alpha$ of the connectivity of the existing node. Algorithms for generating growing networks very quickly in parallel are described and studied. The sublinear and superlinear cases require distinct algorithms. As a result, there is a discontinuous transition in the parallel complexity of sampling these networks corresponding to the discontinuous structural transition at $\alpha=1$, where the networks become scale free. For $\alpha>1$ networks can be generated in constant time while for $0 \leq \alpha < 1$ logarithmic parallel time is required. The results show that these networks have little depth and embody very little history dependence despite being defined by sequential growth rules.