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MATI: An efficient algorithm for influence maximization in social networks

Author

Listed:
  • Maria-Evgenia G Rossi
  • Bowen Shi
  • Nikolaos Tziortziotis
  • Fragkiskos D Malliaros
  • Christos Giatsidis
  • Michalis Vazirgiannis

Abstract

Influence maximization has attracted a lot of attention due to its numerous applications, including diffusion of social movements, the spread of news, viral marketing and outbreak of diseases. The objective is to discover a group of users that are able to maximize the spread of influence across a network. The greedy algorithm gives a solution to the Influence Maximization problem while having a good approximation ratio. Nevertheless it does not scale well for large scale datasets. In this paper, we propose Matrix Influence, MATI, an efficient algorithm that can be used under both the Linear Threshold and Independent Cascade diffusion models. MATI is based on the precalculation of the influence by taking advantage of the simple paths in the node’s neighborhood. An extensive empirical analysis has been performed on multiple real-world datasets showing that MATI has competitive performance when compared to other well-known algorithms with regards to running time and expected influence spread.

Suggested Citation

  • Maria-Evgenia G Rossi & Bowen Shi & Nikolaos Tziortziotis & Fragkiskos D Malliaros & Christos Giatsidis & Michalis Vazirgiannis, 2018. "MATI: An efficient algorithm for influence maximization in social networks," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-21, November.
    • Handle: RePEc:plo:pone00:0206318
    DOI: 10.1371/journal.pone.0206318
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
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