MATI: An efficient algorithm for influence maximization in social networks

Maria Evgenia G. Rossi (1) , Bowen Shi (1) , Nikolaos Tziortziotis (1) , Fragkiskos Malliaros (2, 3) , Christos Giatsidis (1) , Michalis Vazirgiannis (4, 1)

1 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
2 CVN - Centre de vision numérique
3 GALEN-POST - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
4 AUEB - Athens University of Economics and Business

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.

Domains: Computer Science [cs]; Social and Information Networks [cs.SI]

Maria Evgenia G. Rossi, Bowen Shi, Nikolaos Tziortziotis, Fragkiskos Malliaros, Christos Giatsidis, et al.. MATI: An efficient algorithm for influence maximization in social networks. PLoS ONE, 2018, ⟨10.1371/journal.pone.0206318⟩. ⟨hal-01950273⟩