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    樊鹏翼 王 晖 姜志宏 李 沛. 微博网络测量研究[J]. 计算机研究与发展, 2012, 49(4): 691-699.
    引用本文: 樊鹏翼 王 晖 姜志宏 李 沛. 微博网络测量研究[J]. 计算机研究与发展, 2012, 49(4): 691-699.
    Fan Pengyi, Wang Hui, Jiang Zhihong, and Li Pei. Measurement of Microblogging Network[J]. Journal of Computer Research and Development, 2012, 49(4): 691-699.
    Citation: Fan Pengyi, Wang Hui, Jiang Zhihong, and Li Pei. Measurement of Microblogging Network[J]. Journal of Computer Research and Development, 2012, 49(4): 691-699.

    微博网络测量研究

    Measurement of Microblogging Network

    • 摘要: 随着移动通信和Web技术的不断突破,以微博为代表的在线社会网络在中国广泛发展起来,越来越多的人开始使用微博进行信息分发和舆论传播.为了了解中国微博网络中的拓扑结构特征和用户行为特征等内在信息,对国内最大的微博系统——新浪微博——开展了主动测量,并结合已有的在线社会网络测量结果,对新浪微博的网络拓扑和用户行为特征进行了分析和比较.主要发现包括:1)新浪微博网络具有小世界特性;2)新浪微博网络的入度分布属于幂次分布,而出度分布表现为某种分段幂率函数;3)与类似社会网络相比,新浪微博网络的出入度不具有相关性;4)新浪微博网络属于同配网络;5)新浪微博用户发博时间具有明显的日分布和周分布模式;6)新浪微博用户博文数目分布表现为威布尔分布;7)新浪微博用户博文的转发和评价行为具有很强的相关性,且博文转发概率要高于评价概率.这些测量研究和发现不仅有助于设计出符合中国微博网络结构特征的数学模型和计算模型,也是实现对微博舆论的监测、引导、控制等方面的重要依据和基础.

       

      Abstract: With the breakthrough of mobile communications and Web technology, online social network led by microblog has developed widely in China. More and more people begin to share information and opinions through microblogging system. In order to gain insights into the topological characteristics of microblogging network and online user behavior characteristics, we launch an active measurement of Sina microblog which is the biggest microblogging system in China. This paper analyzes the results from our measurement and investigates on topological characteristics and user behavior patterns in Sina microblog, compared with existing results via measuring other online social networks. Our major findings include: 1) Sina microblog has apparent smalll-world effect; 2) While the indegree of Sina microblog follows power-law distribution, the outdegree distribution appears to have multiple separate power-law regimes with different exponents; 3) Unlike other online social networks, Sina microblog has weak correlation of indegree and outdegree; 4) The overlay graph of Sina microblog appears assortative mixing; 5)Tweeting time of users exhibits daily and weekly patterns; 6) The number of tweets in Sina microblog approaches Weibull distribution; 7) Actions of retweeting and replying have strong correlation in Sina microblog, and the probability of retweeting is higher than that of replying. These research and findings will be helpful not only for designing mathematical or computational models which are coincident with actual microblogging characteristics in China, but also for monitoring,directing and dominating of microblogging system.

       

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