{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T06:56:41Z","timestamp":1769065001189,"version":"3.49.0"},"reference-count":68,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06n07","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advs. Complex Syst."],"published-print":{"date-parts":[[2018,9]]},"abstract":"<jats:p>Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes\u2019 opinions have not been considered. In this paper, a modified version of influence maximization which is named as \u201copinion-aware influence maximization\u201d (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms.<\/jats:p>","DOI":"10.1142\/s0219525918500224","type":"journal-article","created":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T04:20:48Z","timestamp":1542687648000},"page":"1850022","source":"Crossref","is-referenced-by-count":6,"title":["OPINION-AWARE INFLUENCE MAXIMIZATION: HOW TO MAXIMIZE A FAVORITE OPINION IN A SOCIAL NETWORK?"],"prefix":"10.1142","volume":"21","author":[{"given":"MEHRDAD AGHA MOHAMMAD ALI","family":"KERMANI","sequence":"first","affiliation":[{"name":"Department of Process Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran"}]},{"given":"REZA","family":"GHESMATI","sequence":"additional","affiliation":[{"name":"Amirkabir University of Technology, Tehran 15875-4413, Iran"}]},{"given":"MASOUD","family":"JALAYER","sequence":"additional","affiliation":[{"name":"Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy"}]}],"member":"219","published-online":{"date-parts":[[2019,1,9]]},"reference":[{"key":"S0219525918500224BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s13235-010-0004-1"},{"key":"S0219525918500224BIB002","doi-asserted-by":"publisher","DOI":"10.1002\/dac.3090"},{"key":"S0219525918500224BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16619-3_10"},{"key":"S0219525918500224BIB004","first-page":"29","volume-title":"Social Learning: Psychological and Biological Perspectives","author":"Boyd R.","year":"1988"},{"key":"S0219525918500224BIB005","volume-title":"Not by Genes Alone","author":"Richerson P. 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