发表机构
National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对社交网络中的用户影响力分析问题，提出了三种基于转发行为和@符号的算法，并通过与训练数据对比验证了其有效性。
AI 中文摘要
在互联网时代，谣言和口碑传播的速度与信息扩散的高速公路一样快。社交网络在庞大的互联网中扮演着相当重要的角色。如今，社交网络已成为我们生活中不可或缺的一部分，尤其是对政府和企业而言。社交网络成为一个复杂的信息扩散网络，用户作为节点，用户之间的关系作为载体。在本文中，我们提出了三种基于用户转发微博行为和微博中@符号来计算用户影响力的算法。我们通过将我们的工作结果与数据集中的训练数据进行比较来评估算法的有效性，最终证明我们的算法效果良好。
英文摘要
Rumor and word of mouth spread at the same speed as the highway of information diffusion in the age of the internet. Social networks play quite an important role in the huge internet. Nowadays, social networks have become indispensable in our lives, especially for the government and enterprises. A social network becomes a complex information diffusion network with users working as nodes and the relationships between users working as the vehicle. In this paper, we propose three kinds of algorithms for computing user influence based on the behavior of a user's forwarding microblogs and the symbol of @ in microblogs. We evaluate the effectiveness of the algorithms by comparing the results of our work with the training data in the dataset, and in the end, it proves that our algorithms work well.
CommentsCST2016, Shenzhen, China, January 2016. 13 pages
