arXiv：cs.LG· Lucas C. S. Oliveira, Michiel Rollier, Jan Baetens, Odemir M. Bruno·· 3 小时前
Life-Like Network Automaton 规则的"锯齿度"可作为分类性能指标
Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance
AI 导读
研究发现 Life-Like Network Automata（LLNA）的规则空间可由"锯齿度"（jaggedness）这一性质结构化描述，该指标可量化 LLNA 转移函数与锯齿形状的相似程度，并作为混沌性与敏感性的理论代理。基于锯齿度的启发式搜索策略在实验中达到距全局最优 5% 以内的分类准确率，同时将计算开销较穷举搜索降低 90%。
正文
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.
| Subjects: | Machine Learning (cs.LG); Cellular Automata and Lattice Gases (nlin.CG) |
| Cite as: | arXiv:2610.10867 [cs.LG] |
| (or arXiv:2610.10867v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10867 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lucas Oliveira [view email]
[v1]
Wed, 7 Oct 2026 20:12:41 UTC (2,676 KB)
来源：arXiv：cs.LG · arxiv.org