Maximizing positive influence in competitive social networks: A trust-based solution
Maximizing positive influence in competitive social networks: A trust-based solution
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DOI：
10.1016/j.ins.2020.09.002
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发表时间：
2021-02-06
影响因子：
8.1
通讯作者：
Wu, Min
中科院分区：
文献类型：
--
作者：
Wang, Feng;She, Jinhua;Wu, Min
在线社交网络为用户传播想法、产品、意见和许多其他项目提供了便利，这些项目与不同项目竞争影响力传播。如何准确地建模竞争影响力的传播仍然是一个具有挑战性的问题。几乎所有的方法都忽略了信任关系在竞争影响力传播中的作用。最大化竞争影响力旨在检测具有竞争级联的社交网络中的前k个积极或消极影响力用户。然而，找到这个问题的最佳解决方案是NP难的。本研究针对上述三个问题，提出一个基于信任的解决方案。首先，我们建立了一个新的基于信任的竞争影响力扩散模型，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，我们通过启发式剪枝方法，提出了一个基于信任的竞争影响最大化算法。已经对合成和真实世界的数据集进行了广泛的比较。通过分析竞争影响的传播和检测种子集的时间复杂度，验证了该方法的有效性和效率。此外，我们的方法比现实世界社交网络上的其他基线更实用。(c)2020爱思唯尔公司All rights reserved.
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. (c) 2020 Elsevier Inc. All rights reserved.