Departamento de Engenharia de Produção, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
PLoS One. 2013 Dec 6;8(12):e80783. doi: 10.1371/journal.pone.0080783. eCollection 2013.
We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution.
我们在这里讨论复杂网络演化的问题。分析复杂网络的拓扑演化对于预测其未来起着至关重要的作用。虽然已经在这个问题上做了大量的工作，但到目前为止，很少有研究关注信息论量化指标如何应用于网络演化的特征描述。为了动态捕捉网络演化的拓扑变化，我们提出了一个模型，通过使用均方根误差（Jensen-Shannon 散度）与平均度数和聚类系数相结合，能够量化和再现给定网络的几个特征。为了支持我们的假设，我们通过复制知名模型和真实系统的演化来测试该模型。结果表明，该方法能够模拟测试网络。通过使用这个模仿模型，用户可以分析网络随时间的行为，也可以推测其演化的主要驱动因素，并提供一个预测其演化的框架。
