Abstract
Finding the most influential nodes is an important issue in social network analysis. To tackle this issue, Kempe et al. proposed the natural greedy strategy, which, although provides a good approximation, suffers from high computation cost on estimating the influence function even if adopting an efficient optimization. In this paper, we propose a simple yet effective evaluation, the expectation, to estimate the influence function. We formulate the expectation of the influence function and its marginal gain first, then give bounds to the expectation of marginal gains. Based on the approximation to the expectation, we put forward a new greedy algorithm called Greedy Estimate-Expectation (GEE), whose advantage over the previous algorithm is to estimate marginal gains via expectation rather than running Monte-Carlo simulation. Experimental results demonstrate that our algorithm can effectively reduce the running time while maintaining the influence spread.
This work is supported in part by the National High-Tech Research and Development Plan of China (863) under Grant No. 2006AA01Z177, the 973 Program of China under Grant No. 2009CB320705, the NSFC Project under Grant No. 60873027 and Jiangsu Provincial NSF Project under Grant No. BK2006115.
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Zhang, Y., Gu, Q., Zheng, J., Chen, D. (2010). Estimate on Expectation for Influence Maximization in Social Networks. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_13
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DOI: https://doi.org/10.1007/978-3-642-13657-3_13
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