Evolution model for scientific collaboration network with local-world information

In order to reveal the intrinsic properties of scientific collaboration networks, a new local-world evolution model on a scientific collaboration network is proposed by analysing the network growth mechanism. The act degree as the measurement of preferential attachment is taken, and the local-world information of nodes is taken into account. Analysis and simulation show that the node degree and the node strength obey the power-law distribution. Low average path length and high clustering coefficient are approved. Experiment indicates that the model can depict efficiently the topological structure and statistical characteristics of real-life scientific collaboration networks.