NLL: A Complex Network Model with Compensation for Enhanced Connectivity

The canonical scale-free model to describe complex networks is BA model with an power-law exponent γ = 3. Researchers further propose DS model (1 <; γ ≤ 4) to consider link failure besides node growth in preferential attachment. However, both models assume globally preferential attachment which is difficult to achieve in real networks. This paper proposes a new scale-free model, i.e. Neighborhood Log-on and Log-off model (NLL) which considers locally preferential connectivity. NLL incorporates both node growth and removal in topology evolvement. Unlike BA and DS, NLL adds compensation mechanism to enhance connectivity. The analysis shows that NLL has 1 <; γ ≤ 3. We conduct simulations to evaluate NLL performance and show that, NLL has short average path length and large clustering coefficient, compared with BA and DS models.