Abstract
Complex Network (CN) classification requires high-level structural characterizations that are both scale-invariant and computationally efficient. Methods based on Life-Like Network Automata (LLNA) offer an interesting way to extract network descriptors by leveraging emergent temporal patterns without requiring provided features, but their efficacy is bottlenecked by a high-cost combinatorial optimization problem: the selection of the automaton transition rule. While current literature relies on exhaustive searches that are unfeasible for large-scale applications, this work reveals that the rule space is fundamentally structured by a property we term ``jaggedness'', that quantifies the resemblance of a LLNA transition function with a sawtooth shape. We demonstrate that this metric acts as a theoretical proxy for chaoticity and sensitivity -- properties essential for generating discriminative dynamic behaviors among network categories. Moreover, we introduce a heuristic search strategy that uses jaggedness to guide the rule selection. Experimental results show that our approach achieves classification accuracies within 5% of the global optimum while reducing computational overhead by 90% compared to exhaustive approach. Our findings provide a novel, efficient, framework for optimizing automata-based methods for pattern recognition.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Oliveira, L. C. S., Rollier, M., Baetens, J., & Bruno, O. M. (2026). Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance. https://omanscience.com/en/articles/shape-irregularity-of-life-like-network-automaton-rules-as-an-indicator-of-classification-performance
MLA 9
Oliveira, Lucas C. S., et al. "Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance." https://omanscience.com/en/articles/shape-irregularity-of-life-like-network-automaton-rules-as-an-indicator-of-classification-performance.
Chicago (author–date)
Oliveira, Lucas C. S., Michiel Rollier, Jan Baetens, and Odemir M. Bruno. 2026. "Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance." https://omanscience.com/en/articles/shape-irregularity-of-life-like-network-automaton-rules-as-an-indicator-of-classification-performance.
Harvard
Oliveira, L. C. S., Rollier, M., Baetens, J. and Bruno, O. M. (2026) 'Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance', Available at: https://omanscience.com/en/articles/shape-irregularity-of-life-like-network-automaton-rules-as-an-indicator-of-classification-performance.
Vancouver
Oliveira LCS, Rollier M, Baetens J, Bruno OM. Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance. https://omanscience.com/en/articles/shape-irregularity-of-life-like-network-automaton-rules-as-an-indicator-of-classification-performance
IEEE
L. C. S. Oliveira, M. Rollier, J. Baetens, and O. M. Bruno, "Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance," https://omanscience.com/en/articles/shape-irregularity-of-life-like-network-automaton-rules-as-an-indicator-of-classification-performance.