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Advances in Knowledge Discovery and Data Mining

Volume 6118 of the series Lecture Notes in Computer Science pp 99-106

Estimate on Expectation for Influence Maximization in Social Networks

  • Yao ZhangAffiliated withState Key Lab. for Novel Software and Technology, Nanjing University
  • , Qing GuAffiliated withState Key Lab. for Novel Software and Technology, Nanjing University
  • , Jun ZhengAffiliated withState Key Lab. for Novel Software and Technology, Nanjing University
  • , Daoxu ChenAffiliated withState Key Lab. for Novel Software and Technology, Nanjing University

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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.