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Calling patterns in human communication dynamics

机译：人类交流动态中的呼叫模式

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摘要

Modern technologies not only provide a variety of communication modes (e.g., texting, cell phone conversation, and online instant messaging), but also detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts. Here, we study intercall duration of communications of the 100,000 most active cell phone users of a Chinese mobile phone operator. We confirm that the intercall durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the intercall durations follow a power-law distribution for only 3,460 individuals (3.46%). The intercall durations for the majority (73.34%) follow a Weibull distribution. We quantify individual users using three measures: out-degree, percentage of outgoing calls, and communication diversity. We find that the cell phone users with a power-law duration distribution fall into three anomalous clusters: robot-based callers, telecom fraud, and telephone sales. This information is of interest to both academics and practitioners, mobile telecom operators in particular. In contrast, the individual users with a Weibull duration distribution form the fourth cluster of ordinary cell phone users. We also discover more information about the calling patterns of these four clusters (e.g., the probability that a user will call the c_r-th most contact and the probability distribution of burst sizes). Our findings may enable a more detailed analysis of the huge body of data contained in the logs of massive users.
机译：现代技术不仅提供了多种通信模式（例如，短信，手机通话和在线即时消息传递），而且还提供了个人之间这些通信的详细电子踪迹。这些电子迹线表明相互作用发生在时间突发中。在这里，我们研究了中国手机运营商的100,000个最活跃的手机用户的通话间通话时间。我们确认，通话持续时间遵循幂律分布，并且在总体水平上呈指数截止，但是在关注单个用户时会发现差异。我们在个人层面进行统计检验，发现通话持续时间仅遵循3,460个人（3.46％）的幂律分布。大多数（73.34％）的通话时间均遵循Weibull分布。我们使用三种度量来量化单个用户：出站程度，去电百分比和通信多样性。我们发现具有幂律持续时间分布的手机用户可以分为三个异常集群：基于机器人的呼叫者，电信欺诈和电话销售。该信息对于学者和从业人员，尤其是移动电信运营商都非常重要。相反，具有威布尔持续时间分布的个人用户构成普通手机用户的第四类。我们还发现了有关这四个集群的呼叫模式的更多信息（例如，用户将呼叫第c_r个最频繁联系的概率以及突发大小的概率分布）。我们的发现可以对海量用户日志中包含的大量数据进行更详细的分析。

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  • 作者

    Zhi-Qiang Jiang; Wen-Jie Xie; Ming-Xia Li; Boris Podobnik; Wei-Xing Zhou; H. Eugene Stanley;

  • 作者单位

    School of Business, School of Science, and Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;

    School of Business, School of Science, and Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;

    School of Business, School of Science, and Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;

    Department of Physics and Center for Polymer Studies, Boston University, Boston, MA 02215,Zagreb School of Economics and Management, 10000Zagreb, Croatia,Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia;

    School of Business, School of Science, and Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;

    School of Business, School of Science, and Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    human dynamics; phone user categorization; social science; nonlinear dynamics; social networks;

    机译：人类动力;电话用户分类;社会科学;非线性动力学;社交网络;

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