Identifying Influential Rumor Spreader in Social Network

Abstract

It is of great significance to identify influential rumor spreaders for preventing and controlling the rumor propagation. In this paper, on four real social networks, based on the classical rumor model and combining one-to-many modes of propagation, we investigate the rumor propagation by Monte Carlo simulations when the spreading rate is small. Firstly, we layer the network nodes according to network characteristics. If the assortative coefficient is positive, we layer the network nodes by the degree centrality and the nodes with large degree are in high layers. If the assortative coefficient is negative, we layer the network nodes by the K-Shell method and the nodes with large Ks value are in high layers. Then the performance of nodes in different layers as origination of rumors and as informed nodes is investigated. We find that the propagation size is larger and the peak prevalence of the rumor is reached in a shorter time when the nodes in higher layers act as origination. Moreover, when the nodes in higher layer are not the origination of the rumor, they are more likely to be informed and they are informed more faster, and they terminate propagation faster. That is, their attendance is more beneficial to propagation size, peak prevalence, and the arrival time of peak prevalence. The conclusion can provide powerful theoretical support for controlling rumor propagation or enhancing information transmission.