Mnasri Wassim, Azaouzi Mehdi, Romdhane Lotfi Ben
MARS Research Laboratory LR17ES05, University of Sousse, Sousse, Tunisia.
L3i, La Rochelle University, La Rochelle, France.
Appl Intell (Dordr). 2021;51(10):7365-7383. doi: 10.1007/s10489-021-02203-x. Epub 2021 Mar 8.
Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is prone to grow exponentially, which makes it difficult to analyze. Critically, most of approaches in the literature focus only on modeling structural properties, ignoring the social behavior in the relations between users. For this, we tend to parallelize the influence maximization task based on social behavior. In this paper, we introduce a new parallel algorithm, named PSAIIM, for identification of influential users in social network. In PSAIIM, we uses two semantic metrics: the user's interests and the dynamically-weighted social actions as user interactive behaviors. In order to overcome the size of actual real-world social networks and to minimize the execution time, we used the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes. Experimental results on real-world networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms, especially in the speed of calculation.
社交网络中的影响力最大化是指寻找那些能最大限度促进信息传播或产品采用的有影响力用户的过程。社交网络易于呈指数级增长，这使其难以分析。关键的是，文献中的大多数方法仅专注于对结构属性进行建模，而忽略了用户之间关系中的社会行为。为此，我们倾向于基于社会行为将影响力最大化任务并行化。在本文中，我们引入了一种名为PSAIIM的新并行算法，用于识别社交网络中的有影响力用户。在PSAIIM中，我们使用两种语义度量：用户兴趣和作为用户交互行为的动态加权社会行为。为了克服实际真实世界社交网络的规模并最小化执行时间，我们利用社区结构在机器的CPU架构上实现完美并行，以计算一组最优的有影响力节点。在真实世界网络上的实验结果表明，与现有的最先进影响力最大化算法相比，该方法是有效的，尤其是在计算速度方面。
