The Impact of a New Algorithm of Complex Network Based on ωPageRank

Abstract

This paper proposes and develops a progressive network model to examine the influence of entities within networks with different scales. In the Model of Citation network, it divides the influencing factor into two types of measurements, and the properties of nodes within networks mainly depend on the relative attributes. Also, weight matrix is employed to design an optimized algorithm of Google search algorithm PageRank, which is simply named as ωPageRank. A new approach has been proposed to identify a sub network on the basis of arc weights, with an example of the networks of 30 popular music users in Twitter specifically illustrated. Furthermore, it restricts the circumstance parameters and chooses the user with the lowest influence rank to be the experimental object. Also, with the help of the proposed model, the best candidate could be identified who needs to get acquainted with specific musics in order to boost the mathematical influence. Finally, the improvement of ωPageRank algorithm needs to put an emphasis on aspects of efficiency, accuracy and the application range.