Influence of the nearest-neighbor connections on shaping weighted evolving network

• We propose a novel local-world model with the nearest-neighbor connections.
• This model displays the same exponent functions as the ones in the BBV model.
• This model has a high clustering coefficient and the varying averaged shortest path length.
• The structure is significant hierarchical and weakens the network synchronization.

This paper proposes an extended local-world evolving network model consisting of global strength-driven preferential attachment for one central node, and local weight-driven preferential attachment for nearest neighbors of the central node. Analytical predictions and numerical simulations were executed for network evolutions and distributions. The obtained power-law behaviors display the same exponent functions as the ones in a classic model. More comparisons between these two models were made to investigate the structural differences that the nearest-neighbor connections result in. Compared with the counterpart, the proposed model shows a higher clustering coefficient, the varying average shortest path length and the significant hierarchical organization. our model is generally robustness and yet fragility, and is weaker in synchronizability than the counterpart. All those results are added to our understanding of how the rule of the nearest-neighbor connections affects the characteristics of weighted evolving network.