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Influence maximization in social networks: Considering both positive and negative relationships

机译:社交网络中的影响最大化:同时考虑正面和负面关系

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Now a days, the viral marketing has been extensively increased on online social networks, thus increasing the research work on the influence maximization algorithms in social networks. Motivated by this viral marketing strategies, many of the researches proposed few fundamental algorithms to find the subset of individuals to promote a product where the goal is to trigger maximum number of individuals in the network. The problem here is which subset of individuals to pick and why? This problem has been studied by many researches. However, those studies focused on social networks with friendship relations, but to be practical the foe relationship also exists in social networks. Our study is to propose a new diffusion algorithm considering both the friend and foe relationship types in online social networks (OSNs), where the network model would be with both positive and negative edges. We first provide our newly proposed diffusion model, define the influence maximization problem i.e. Positive Opinion Influential Node Set (POINS) selection problem, provide the solution for POINS problem and perform simulations to the social networks with both friend and foe relationships.
机译:如今,在线社交网络上的病毒式营销已得到大量增加,从而增加了对社交网络中影响力最大化算法的研究工作。受这种病毒式营销策略的激励,许多研究提出了一些基本算法来寻找个人子集来推广产品,而该产品的目标是触发网络中最大数量的个人。这里的问题是要挑选哪个个人子集,为什么?许多研究已经对此问题进行了研究。但是,这些研究的重点是具有友谊关系的社交网络,但实际上,社交网络中也存在敌对关系。我们的研究是提出一种新的扩散算法,同时考虑在线社交网络(OSN)中的敌对关系类型,其中网络模型将同时具有正向和负向边缘。我们首先提供新提出的扩散模型,定义影响最大化问题,即正面意见影响节点集(POINS)选择问题,提供POINS问题的解决方案,并对具有亲友关系的社交网络进行仿真。

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