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SMG: Fast scalable greedy algorithm for influence maximization in social networks

Mehdi Heidari, Masoud Asadpour and Hesham Faili

Physica A: Statistical Mechanics and its Applications, 2015, vol. 420, issue C, 124-133

Abstract: Influence maximization is the problem of finding k most influential nodes in a social network. Many works have been done in two different categories, greedy approaches and heuristic approaches. The greedy approaches have better influence spread, but lower scalability on large networks. The heuristic approaches are scalable and fast but not for all type of networks. Improving the scalability of greedy approach is still an open and hot issue. In this work we present a fast greedy algorithm called State Machine Greedy that improves the existing algorithms by reducing calculations in two parts: (1) counting the traversing nodes in estimate propagation procedure, (2) Monte-Carlo graph construction in simulation of diffusion. The results show that our method makes a huge improvement in the speed over the existing greedy approaches.

Keywords: Social networks; Influence maximization; Scalable greedy; State machine Monte-Carlo; Propagation model; Viral marketing (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:420:y:2015:i:c:p:124-133

DOI: 10.1016/j.physa.2014.10.088

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Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

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