Abstract: To select key users with great information dissemination capability efficiently and effectively from large-scale social networks and corresponding historical user massages, an approach for selecting key users was proposed. Firstly, the structure information of the social network was used to construct the directed graph with the user as the node. Based on the Spark calculation framework, the weights of user activity, transmission interaction and information quantity were quantitatively calculated by the historical data of the message, so as to construct a dynamic weighted graph model of social networks. Then, the measurement for user's information dissemination capacity was established based on PageRank and the Spark-based algorithm was given correspondingly for large-scale social networks. Further more, the algorithm for d-distance selection of key users was given to make the overlap of information dissemination ranges of different key users be as less as possible by multiple iterations. The experimental results based on Sina Weibo datasets show that the proposed approach is efficient, feasible and scalable, and can provide underlying techniques to control the spread of bad news and monitor public opinions to a certain extent.

Key words: large-scale social network, information dissemination capacity, key user, PageRank, Spark

摘要： 针对大规模社交网络及其用户发布消息的历史数据，如何快速有效地选取具有较强信息传播能力的关键用户，提出了一种关键用户选取方法。首先，利用社交网络的结构信息，构建以用户为节点的有向图，利用用户发布消息的历史数据，基于Spark计算框架，定量计算由用户活跃度、转发交互度和信息量占比刻画的权重，从而构建社交网络的有向带权图模型；然后，借鉴PageRank算法，建立用户信息传播能力的度量机制，给出基于Spark的大规模社交网络中用户信息传播能力的计算方法；进而，给出基于Spark的d-距选取算法，通过多次迭代，使得所选取的不同关键用户的信息传播范围尽量少地重叠。建立在新浪微博数据上的实验结果表明，所提方法具有高效性、可行性和可扩展性，对于控制不良突发信息传播、社交网络舆情监控具有一定的支撑作用。

关键词: 大规模社交网络, 信息传播能力, 关键用户, PageRank, Spark