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Maximizing positive influence in competitive social networks: A trust-based solution
Information Sciences ( IF 8.1 ) Pub Date : 2020-09-08 , DOI: 10.1016/j.ins.2020.09.002
Feng Wang , Jinhua She , Yasuhiro Ohyama , Wenjun Jiang , Geyong Min , Guojun Wang , Min Wu

Online social networks provide convenience for users to propagate ideas, products, opinions, and many other items that compete with different items for influence spread. How to accurately model the spread of competitive influence is still a challenging problem. Almost all reported methods ignore the effect of trust relationships in the spread of competitive influence. Maximizing competitive influence aims to detect the top-k positive or negative influential users in social networks with competing cascades. However, finding an optimal solution to this problem is NP-hard. This study focuses on exploring the above three issues by devising a trust-based solution. First, we established a new model of trust-based competitive influence diffusion that simulates the spread of positive and negative influence. Second, we estimated trust values via generalized network flows and used these values to calculate influence probabilities. Finally, we developed an efficient algorithm of trust-based competitive influence maximization through a heuristic pruning method. Extensive comparisons have been conducted on synthetic and real-world datasets. The effectiveness and efficiency of our approach are verified by analyzing the spread of competitive influence and the time complexity of detecting seed sets. Moreover, our approach is more practical than other baselines on real-world social networks.



中文翻译:

在竞争性社交网络中最大化积极影响:基于信任的解决方案

在线社交网络为用户提供了传播思想,产品,观点以及许多其他与不同项目竞争以扩大影响力的项目的便利。如何准确地模拟竞争影响力的传播仍然是一个具有挑战性的问题。几乎所有报道的方法都忽略了信任关系对竞争影响力传播的影响。最大限度地提高竞争力的影响,旨在检测顶ķ具有竞争级联的社交网络中的正面或负面影响力用户。但是,找到此问题的最佳解决方案是NP-hard。本研究致力于通过设计基于信任的解决方案来探索上述三个问题。首先,我们建立了基于信任的竞争影响力扩散的新模型,该模型模拟了正面和负面影响力的扩散。其次,我们通过广义网络流量估计信任值,并使用这些值来计算影响概率。最后,我们通过启发式修剪方法开发了一种有效的基于信任的竞争影响最大化算法。已经对综合和真实数据集进行了广泛的比较。通过分析竞争影响力的扩散和检测种子集的时间复杂性,验证了我们方法的有效性和效率。此外,我们的方法比现实社会网络中的其他基准更实用。

更新日期:2020-09-08
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