{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.10867",
  "author_total": 4,
  "authors": [
    {
      "name": "Lucas C. S. Oliveira"
    },
    {
      "name": "Michiel Rollier"
    },
    {
      "name": "Jan Baetens"
    },
    {
      "name": "Odemir M. Bruno"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5phqgr9ad1kj7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.10867",
  "fulltext_url": null,
  "id": "5phqgr9ad1kj7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.10867",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "nlin.CG"
    ],
    [
      "openalex",
      "1703|Cellular Automata and Applications"
    ]
  ],
  "openalex": "W7221162989",
  "pdf_url": "https://arxiv.org/pdf/2610.10867",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5phqgr9ad1kj7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2610.10867",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.10867"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:nlin.CG",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance",
  "title_text": "Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/5phqgr9ad1kj7",
  "venue": "arXiv",
  "versions": []
}