论文标题
在现实世界中关闭的社交网络中影响最大化
Influence Maximization in Real-World Closed Social Networks
论文作者
论文摘要
在过去的几年中，许多封闭的社交网络（例如WhatsApp和微信）已经出现了，以满足人们对隐私和独立性的日益增长的需求。在封闭的社交网络中，所有用户都不可用发布的内容，或者发件人可以设置谁可以看到张贴的内容的限制。在这种约束下，我们研究了封闭的社交网络中影响最大化的问题。它旨在向用户（不仅是种子用户）推荐有限数量的现有朋友，他们将有助于传播信息，以便可以最大程度地提高种子用户的影响力传播。我们首先证明这个问题是NP-HARD。然后，我们提出了一种高效但有效的方法来增强扩散网络，该网络最初仅由种子用户组成。增强是通过迭代和智能地从原始网络中选择有限数量的边缘来完成的。通过对现实世界中社交网络（包括部署到现实世界应用程序）的广泛实验，我们证明了我们提出的方法的有效性和效率。
In the last few years, many closed social networks such as WhatsAPP and WeChat have emerged to cater for people's growing demand of privacy and independence. In a closed social network, the posted content is not available to all users or senders can set limits on who can see the posted content. Under such a constraint, we study the problem of influence maximization in a closed social network. It aims to recommend users (not just the seed users) a limited number of existing friends who will help propagate the information, such that the seed users' influence spread can be maximized. We first prove that this problem is NP-hard. Then, we propose a highly effective yet efficient method to augment the diffusion network, which initially consists of seed users only. The augmentation is done by iteratively and intelligently selecting and inserting a limited number of edges from the original network. Through extensive experiments on real-world social networks including deployment into a real-world application, we demonstrate the effectiveness and efficiency of our proposed method.
