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Journal articles
MATI: An efficient algorithm for influence maximization in social networks
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 Maxi-mization 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.
Cited literature [37 references]
https://hal-centralesupelec.archives-ouvertes.fr/hal-01950273
Contributor : Fragkiskos Malliaros <>
Submitted on : Monday, December 10, 2018 - 4:26:02 PM
Last modification on : Thursday, July 9, 2020 - 4:06:04 PM
Long-term archiving on: : Monday, March 11, 2019 - 3:50:20 PM
Contributor : Fragkiskos Malliaros <>
Submitted on : Monday, December 10, 2018 - 4:26:02 PM
Last modification on : Thursday, July 9, 2020 - 4:06:04 PM
Long-term archiving on: : Monday, March 11, 2019 - 3:50:20 PM
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MATI_Plos_One_2018.pdf
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