%0 Journal Article
%A LI Minjia
%A XU Guoyan
%A ZHANG Wangjuan
%A ZHU Shuai
%T Influence maximization algorithm based on structure hole and degree discount
%D 2018
%R 10.11772/j.issn.1001-9081.2018040920
%J Journal of Computer Applications
%P 3419-3424
%V 38
%N 12
%X The existing Influence Maximization (IM) algorithms of social network have the problem of low influence range caused by only selecting local optimal nodes at present. In order to solve the problem, considering the propagation advantage of core node and structure hole node, a maximization algorithm based on Structure Hole and Degree Discount (SHDD) was proposd. Firstly, the ideas of structure hole and centrality degree were integrated and applied to the influence maximization problem, and the factor *α* combining the structure hole node and the core node was found out to play the maximum propagation function, which made the information spread more widely to increase the influence of the whole network. Then, in order to highlight the advantages of the integration of two ideas, the influence of second-degree neighbor was added to the evaluation criteria of structure hole to select the structure hole node. The experimental results on data sets of different scales show that, compared with DegreeDiscount algorithm, SHDD can increase the influence range without consuming too much time, and compared with the Structure-based Greedy (SG) algorithm, SHDD can expand the influence range and reduce the time cost in the network with large clustering coefficient. The proposed SHDD algorithm can maximize the advantages of structure hole node and core node fusion when factor *α* is 0.6, and it can expand the influence range more steadily in the social network with large clustering coefficient.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2018040920