Degree Distribution, Rank-size Distribution, and Leadership Persistence in Mediation-Driven Attachment Networks

Abstract

We investigate the growth of a class of networks in which a new node first picks a mediator at random and connects with $m$ randomly chosen neighbors of the mediator at each time step. We show that degree distribution in such a mediation-driven attachment (MDA) network exhibits power-law $P(k)\sim k^{-\gamma (m)}$ with a spectrum of exponents depending on $m$. To appreciate the contrast between MDA and Barabási-Albert (BA) networks, we then discuss their rank-size distribution. To quantify how long a leader, the node with the maximum degree, persists in its leadership as the network evolves, we investigate the leadership persistence probability $F(\tau )$ i.e. the probability that a leader retains its leadership up to time $\tau$. We find that it exhibits a power-law $F(\tau )\sim {\tau }^{-\theta (m)}$ with persistence exponent $\theta (m)\approx 1.51\forall m$ in the MDA networks and $\theta (m)\to 1.53$ exponentially with $m$ in the BA networks.

7 pages, 8 figures, accepted for publication in Physica A

References in corpus (3)

• Scale-free brain functional networks
• Scale-free network growth by ranking
• Scale-Free Networks Generated By Random Walkers

Cited by in corpus (1)

• COVID-19 Should be Suppressed by Mixed Constraints -- from Simulations on Constrained Scale-Free Networks