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Selection of top-K influential users based on radius-neighborhood degree, multi-hops distance and selection threshold
2018
Journal of Big Data
Influence maximization in the social network becomes increasingly important due to its various benefit and application in diverse areas. In this paper, we propose DERND D-hops that adapt the radius-neighborhood degree to a directed graph which is an improvement of our previous algorithm RND d-hops. Then, we propose UERND D-hops algorithm for the undirected graph which is based on radius-neighborhood degree metric for selection of top-K influential users by improving the selection process of our
doi:10.1186/s40537-018-0137-4
fatcat:pn4mb33b5ngnhf6lf2kn5dongq