Prediction of competitive diffusion on complex networks

In this paper, we study the prediction problem of diffusion process on complex networks in competitive circumstances. With this problem solved, the competitors could timely intervene the diffusion process if needed such that an expected outcome might be obtained. We consider a model with two groups of competitors spreading opposite opinions on a network. A prediction method based on the mutual influences among the agents is proposed, called Influence Matrix (IM for short), and simulations on real-world networks show that the proposed IM method has quite high accuracy on predicting both the preference of any normal agent and the final competition result. For comparison purpose, classic centrality measures are also used to predict the competition result. It is shown that PageRank, Degree, Katz Centrality, and the IM method are suitable for predicting the competition result. More precisely, in undirected networks, the IM method performs better than these centrality measures when the competing group contains more than one agent; in directed networks, the IM method performs only second to PageRank.