Wroclaw University of Science and Technology, Department of Computational Intelligence, Wroclaw, 50-370, Poland.
Rensselaer Polytechnic Institute, Department of Computer Science, Troy, NY, 12180-3590, USA.
Sci Rep. 2018 Oct 24;8(1):15697. doi: 10.1038/s41598-018-32571-3.
Human communication is commonly represented as a temporal social network, and evaluated in terms of its uniqueness. We propose a set of new entropy-based measures for human communication dynamics represented within the temporal social network as event sequences. Using real world datasets and random interaction series of different types we find that real human contact events always significantly differ from random ones. This human distinctiveness increases over time and by means of the proposed entropy measures, we can observe sociological processes that take place within dynamic communities.
人类交流通常被表示为一个时间社交网络，并根据其独特性进行评估。我们提出了一套新的基于熵的度量方法，用于表示时间社交网络中的事件序列的人类交流动态。使用真实世界数据集和不同类型的随机交互序列，我们发现真实的人类接触事件总是与随机事件有显著的区别。这种人类独特性随着时间的推移而增加，通过我们提出的熵度量方法，我们可以观察到动态社区中发生的社会学过程。
