Significance of End User Ranking Mechanism for Community Detection

Abstract

An improved technique for ranking node importance based on the division of complex network communities (NI-DCN) is proposed because Page Rank results are too concentrated and do not consider the structural properties of complex network communities. According to the results of the Label Propagation Algorithm's (LPA) community division of the complex network, the internal and external connections of the community are transformed into the probability representation of community selection; based on the community selection probability, a certain proportion of candidate key nodes are extracted from each community; these candidates are then evaluated. To obtain the key node sorting results, the nodes are reordered. The SIR propagation performance investigation uses experimental data from four existing complex networks to compare with the existing algorithm. The experimental results indicate that the nodes chosen by the NI-DCN algorithm have a more significant impact on the overall efficacy of the network's propagation. Moreover, the NI-DCN algorithm can effectively rank the significance of the complex network's nodes.