Influence maximization by rumor spreading on correlated networks through community identification

• Published in: Communications in nonlinear science & numerical simulation Vol. 83; p. 105094
• Main Authors: Vega-Oliveros, Didier A., da Fontoura Costa, Luciano, Rodrigues, Francisco Aparecido
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2020; Elsevier Science Ltd
• Subjects: Algorithms; Community identification; Computer simulation; Degree-degree correlation; Greedy algorithms; Influence maximization; Information dissemination; Networks; Propagation; Rumor propagation; Simulation; Spreaders; Studies; Influence maximization; Degree-degree correlation; Community identification; Rumor propagation
• ISSN: 1007-5704; 1878-7274
• DOI: 10.1016/j.cnsns.2019.105094

•Network assortativity impacts on the results of the influence maximization methods.•More spreaders may not provide additional informed nodes at the end of the dynamic.•Selecting the best spreaders by communities performs similarly to the Greedy approach.•It is more suitable and less time-consuming the selection of spreaders by communities. The identification of the minimal set of nodes that maximizes the propagation of information is one of the most relevant problems in network science. In this paper, we introduce a new method to find the set of initial spreaders to maximize the information propagation in complex networks. We evaluate this method in assortative networks and verify that degree-degree correlation plays a fundamental role in the spreading dynamics. Simulation results show that our algorithm is statistically similar, regarding the average size of outbreaks, to the greedy approach in real-world networks. However, our method is much less time consuming than the greedy algorithm.