Influence Maximization in Social Networks using Deterministic Crowding Algorithm
Navid Kaveh1, Mehdi Bateni2

1Navid Kaveh, School of Engineering, Sheikhbahaee University, Isfahan, Iran.
2Mehdi Bateni, School of Engineering, Sheikhbahaee University, Isfahan, Iran.
Manuscript received on 26 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 4258-4265 | Volume-8 Issue-11, September 2019. | Retrieval Number: K24480981119/2019©BEIESP | DOI: 10.35940/ijitee.K2448.0981119
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Abstract: In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others’ behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.
Keywords: Social Network, Influence Spread, Influence Maximization, Explicit Multimodal Evolutionary Algorithms, Deterministic Crowding.
Scope of the Article: Social Networks