Junying Shang
ABSTRACT
In the field of social network analysis, influence maximization is an important research area. The goal is to find K nodes that maximize the final range of influence based on a given spread model. In the study of influence maximization, the research method based on greedy algorithm mainly focuses on how to further reduce Monte-Carlo simulations. In this paper, based on the dynamic update of the upper bound of marginal benefit of node, we propose an Upper Bound Update Greedy (UBUG) algorithm and an Upper Bound Update Heuristic (UBUH) algorithm. Experimental results show that the UBUG algorithm can reduce more MC simulations than the CELF [1] and UBLF [2] algorithms without losing the accuracy of the results. And compared to other heuristic algorithms, the UBUH not only meets the requirements of high calculation speed but also has a good accuracy.
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Index Terms
- Dynamic Update Upper Bounds Influence Maximization Algorithm
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