TY - JOUR ID - TI - Inflfluence Maximization based on a Non-dominated Sorting Genetic Algorithm AU - Elaf Adel Abbas College of Information Technology, University of Babylon, elaf1982adil@gmail.com Huda Naji Nawaf College of Information Technology, University of Babylon, halmamory@itnet.uobabylon.edu.iq PY - 2021 VL - 7 IS - 2 SP - 139 EP - 150 JO - Karbala International Journal of Modern Science مجلة كربلاء العالمية للعلوم الحديثة SN - 2405609X 24056103 AB - Influence Maximization (IM) is a problem represented by a set of users who are specified in advance andare usually called the seed. The latter can influence their friends, who can in turn influence others and soon until it reaches the largest number of users within the network. This issue is of ultimate importance ina variety of fields. In the current study, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) has beenadopted in influence maximization to produce the so-called NSGAII based IM algorithm (NSGAII-IM).Principally, the population should be represented with individuals of variable lengths as the seed group,and the diffusion model should be designed so as to formulate its multi-objective function. In the contextof individual representation, the nodes have been pseudo-randomly chosen using the centrality measures(based on high centrality nodes as degree, closeness, and eigenvector). As for the multi-objectivefunction, increasing the coverage size of influence and decreasing the number of seed nodes as far aspossible have been set as the conflicting objectives. Weighted Integration Cascade (WIC) has beensuggested as an improved version of the Independent Cascade (IC) diffusion model. It has proven to beeffective in the performance of the NSGAII-IM algorithm. In evaluating the proposed optimization model,two real-world social network datasets have been used: Facebook wall posts, and Digg networks. Thealgorithm showed promising results as it could relatively improve the solutions as compared with othermethods, with an increased average of influential spread. Additionally, the WIC model has proven to beeffective through the evaluation of the performance of the NSGAII-IM algorithm with other diffusionmodels.

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