| Full text | |
| Author(s): |
Zielinski, Kallil M. C.
;
Ribas, Lucas C.
;
Machicao, Jeaneth
;
Bruno, Odemir M.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PATTERN RECOGNITION; v. 146, p. 10-pg., 2023-09-29. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 23/04583-2 - Pattern recognition in images based on artificial neural networks and complex systems: from handcrafted descriptor extraction to automated learning |
| Grantee: | Lucas Correia Ribas |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 21/07289-2 - Learning Representations using artificial neural networks and complex networks with applications in sensors and biosensors |
| Grantee: | Lucas Correia Ribas |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 21/08325-2 - An analysis of network automata as models for biological and natural processes |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 20/03514-9 - Evaluation the effects of Brazilian protected areas in local communities based on the use and re-use of biological, environmental and socioeconomic data |
| Grantee: | Marina Jeaneth Machicao Justo |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 22/03668-1 - Analysis of the Dynamic Behavior of Complex Systems and Artificial Neural Networks in Computer Vision and Artificial Intelligence |
| Grantee: | Kallil Miguel Caparroz Zielinski |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis |
| Grantee: | Osvaldo Novais de Oliveira Junior |
| Support Opportunities: | Research Projects - Thematic Grants |