A Network Classification Method based on Density Time Evolution Patterns Extracted from Network Automata

TL;DR

This paper introduces a novel network classification method using density-based time-evolution patterns derived from network automata, significantly improving pattern recognition accuracy on synthetic and real-world networks.

Contribution

It proposes density and state density time-evolution patterns as new descriptors, enhancing network classification beyond binary TEPs used previously.

Findings

Significant accuracy improvements on synthetic networks

Effective classification on real-world network datasets

Potential applicability to image data analysis

Abstract

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.