Empirical analysis of microblog centrality and spread influence based on Bi-directional connection

Abstract

The identifying of the most influential nodes in the complex network is of great significance for information dissemination and control. We collect actual data from Sina Weibo and establish two user relationship networks based on bi-directional concern. By analyzing the statistical characteristics of the network topology, we find that each of them has a small world and scale free characteristics. Moreover, we describe four network centrality indicators, including node degree, closeness, betweenness and k-Core. Through empirical analysis of four-centrality metric distribution, we find that the node degrees follow a segmented power-law distribution; betweenness difference is most significant; both networks possess significant hierarchy, but not all of the nodes with higher degree have the greater k-Core values; strong correlation exists between the centrality indicators of all nodes, but this correlation is weakened in the node with higher degree value. The two networks are used to simulate the information spreading process with the SIR information dissemination model based on infectious disease dynamics. The simulation results show that there are different effects on the scope and speed of information dissemination under different initial selected individuals. We find that the closeness and k-Core can be more accurate representations of the core of the network location than other indicators, which helps us to identify influential nodes in the information dissemination network.