Chunhui Deng
ABSTRACT
With the rapid development of Internet technology, the social network has become more and more complicated and diversified. Excavating the most influential users from social networks become more difficult and important. The information diffusion maximization algorithm can effectively solve the problem. First, in this paper we proposed a new optimal algorithm (NOA) on the information diffusion maximization problem by synthetically considering the theme feature and degree centrality. Second, we presented a new way of representing the user influence based on the user preference. In addition, in order to compare with other methods more extensively and deeply and verify the effectiveness and superiority of the algorithm, the traditional information diffusion models are extended to the topic-based linear threshold model (TLTM) and the topic-based independent cascade model (TICM) which can describe the information diffusion mechanism more accurately. Experimental results show that NOA algorithm proposed in this paper is 5000 times faster than the climbing greedy algorithm in the worst case and also faster than the CELF(Cost Effective Lazy Forward) algorithm. If the nodes selected through NOA algorithm are used as the initial spreading set, the results have a significant improvement than the climbing greedy algorithm in the diffusion range and the actual influence. And the results also show that the new algorithm is really efficient, accurate and stable.
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Index Terms
- A New Optimal Algorithm on Information Diffusion Maximization Problem
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