Network modeling has proven to be an efficient tool for many interdisciplinary areas, including social, biological, transport, and many other real world complex systems. In addition, cellular automata (CA) are a formalism that has been studied in the last decades as a model for exploring patterns in the dynamic spatio-temporal behavior of these systems based on local rules. Some studies explore the use of cellular automata to analyze the dynamic behavior of networks, denominating them as network automata (NA). Recently, NA proved to be efficient for network classification, since it uses a time-evolution pattern (TEP) for the feature extraction. However, the TEPs explored by previous studies are composed of binary values, which does not represent detailed information on the network analyzed. Therefore, in this paper, we propose alternate sources of information to use as descriptor for the classification task, which we denominate as density time-evolution pattern (D-TEP) and state density time-evolution pattern (SD-TEP). We explore the density of alive neighbors of each node, which is a continuous value, and compute feature vectors based on histograms of the TEPs. Our results show a significant improvement compared to previous studies at five synthetic network databases and also seven real world databases. Our proposed method demonstrates not only a good approach for pattern recognition in networks, but also shows great potential for other kinds of data, such as images.

本研究提出了使用密度时间演变(pattern) (D-TEP) 和状态密度时间演变(pattern) (SD-TEP) 作为网络分类任务的特征描述符，并基于直方图计算特征向量，其结果表明相比之前的研究，我们的方法在五个合成网络数据库和七个真实世界数据库中都有显着提升，这不仅是网络中的图案识别的好方法，而且也展示了在其他数据，例如图像，领域中的巨大潜力。

一种基于从网络自动机中提取的密度时间演化模式的网络分类方法