论文标题
骗子更具影响力：欺骗在影响最大化社交网络的影响
Liars are more influential: Effect of Deception in Influence Maximization on Social Networks
论文作者
论文摘要
检测有影响力的用户称为社交网络上的影响最大化问题，是一个重要的图表挖掘问题，其中包括信息传播，市场广告和谣言控制等许多不同的应用程序。文献中有许多研究针对社交网络中有影响力的用户检测问题。尽管当前的方法成功地用于许多不同的应用程序中，但他们认为用户彼此诚实，而忽略了欺骗在社交网络中的作用。另一方面，在社交网络中人类中，欺骗似乎令人惊讶。在本文中，我们研究了欺骗在影响最大化对社交网络的影响。我们首先在社交网络中模拟欺骗。然后，由于最近通过Sheaf Laplacian的观点动态模型，我们对这些网络上的意见动态进行了建模。然后，我们扩展了两种影响力的淋巴结检测方法，即拉普拉斯的中心性和DFF中心性，用于脱皮的拉普拉斯式，以衡量欺骗在影响最大化中的影响。我们对合成和现实世界网络的实验结果表明，骗子比社交网络中的诚实用户更具影响力。
Detecting influential users, called the influence maximization problem on social networks, is an important graph mining problem with many diverse applications such as information propagation, market advertising, and rumor controlling. There are many studies in the literature for influential users detection problem in social networks. Although the current methods are successfully used in many different applications, they assume that users are honest with each other and ignore the role of deception on social networks. On the other hand, deception appears to be surprisingly common among humans within social networks. In this paper, we study the effect of deception in influence maximization on social networks. We first model deception in social networks. Then, we model the opinion dynamics on these networks taking the deception into consideration thanks to a recent opinion dynamics model via sheaf Laplacian. We then extend two influential node detection methods, namely Laplacian centrality and DFF centrality, for the sheaf Laplacian to measure the effect of deception in influence maximization. Our experimental results on synthetic and real-world networks suggest that liars are more influential than honest users in social networks.
