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Volume 2017, Article ID 5049836, 14 pages
Research Article

On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

School of Computer, National University of Defense Technology, Changsha 410073, China

Correspondence should be addressed to Xiaodong Liu; nc.ude.tdun@gnodoaixuil

Received 13 March 2017; Revised 4 July 2017; Accepted 1 August 2017; Published 28 September 2017

Academic Editor: Piotr Brodka

Copyright © 2017 Xiaodong Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Influence maximization problem aims to identify the most influential individuals so as to help in developing effective viral marketing strategies over social networks. Previous studies mainly focus on designing efficient algorithms or heuristics on a static social network. As a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time. In this paper, we propose an incremental approach, IncInf, which can efficiently locate the top- influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to narrow the search space into nodes experiencing major increases or with high degrees. To evaluate the efficiency and effectiveness, we carried out extensive experiments on real-world dynamic social networks: Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static algorithm, IncInf achieves remarkable speedup in execution time while maintaining matching performance in terms of influence spread.