A network classification method based on density time evolution patterns extracted from network automata

Kallil Zielinski

doi:10.1016/j.patcog.2023.109946

2024-02-01

Kallil Zielinski

Lucas C. Ribas

Jeaneth Machicao

Odemir Martinez Bruno *

DOI：10.1016/j.patcog.2023.109946

原文链接

查看原文

摘要

En 中文

Network modeling has proven to be an efficient tool for many interdisciplinary areas, including social, biological, transportation, and various other complex real-world systems. In addition, cellular automata (CA) are a formalism that has received significant attention in recent decades as a model for investigating patterns in the dynamic spatio-temporal behavior of these systems, based on local rules. Some studies investigate the use of cellular automata to analyze the dynamic behavior of networks and refer to them as network automata (NA). Recently, it has been demonstrated that NA is effective for network classification, as it employs a Time-Evolution Pattern (TEP) for feature extraction. However, the TEPs investigated in previous studies consist of binary values (states) that do not capture the intrinsic details of the analyzed network. Therefore, in this work, we propose alternative sources of information that can be used as descriptors for the classification task, which we refer as Density Time-Evolution Pattern (D-TEP) and State Density Time-Evolution Pattern (SD-TEP). We examine the density of alive neighbors of each node, which is a continuous value, and compute feature vectors based on histograms of TEPs. Our results demonstrate significant improvement over previous studies on five synthetic network datasets, as well as seven real datasets. Our proposed method is not only a promising approach for pattern recognition in networks, but also shows considerable potential for other types of data that can be transformed into network.

Keyword:

Complex networks

Cellular automata

Network automata

Pattern recognition