NDLI: In-time estimation for influence maximization in large-scale social networks

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In-time estimation for influence maximization in large-scale social networks

Content Provider	ACM Digital Library
Author	Wu, Qingbo Liu, Xiaodong Wang, Lei Li, Shanshan Liao, Xiangke
Abstract	Influence Maximization aims to find the top-K influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Most of the existing studies focus on greedy algorithms and mainly suffer from low computational efficiency, limiting its application to real-world social networks. In this paper, we propose a novel approach ESMCE that can significantly reduce the running time. Utilizing a power-law exponent supervised Monte Carlo method, ESMCE is able to efficiently estimate the influence spread for nodes with specified precision by randomly sampling only a small portion of child nodes, thus is well suitable for large-scale social networks. Extensive experiments on five real-world social network demonstrate that, compared with state-of-the-art influence maximization algorithms, ESMCE is able to achieve more than an order of magnitude speedup in execution time with only negligible error (2.21% on average) in influence spread.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450311649
DOI	10.1145/2181176.2181179
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Influence maximization Monte-carlo method
Content Type	Text
Resource Type	Article

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