发表机构
Institute of Mathematics and Computer Science; University of São Paulo; BionamiX; Ghent University; São Carlos Institute of Physics（数学与计算机科学研究所; 圣保罗大学; BionamiX; 根特大学; 圣卡洛斯物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出用类生命网络自动机规则的锯齿度作为分类性能的理论代理，引入基于锯齿度的启发式规则选择策略，使分类准确率接近全局最优且计算开销降低90%，为优化自动机模式识别方法提供了高效框架。
AI 中文摘要
复杂网络（Complex Network, CN）分类需要兼具尺度不变性和计算高效性的高级结构表征。基于类生命网络自动机（Life-Like Network Automata, LLNA）的方法提供了一种有趣的途径，可利用涌现的时间模式提取网络描述符，无需提供预设特征，但其效能受限于高成本的组合优化问题：自动机转移规则的选择。现有文献依赖穷举搜索，该方法对于大规模应用不可行，本研究揭示规则空间本质上由一种我们称为“锯齿度（jaggedness）”的属性构建，该属性量化LLNA转移函数与锯齿形状的相似程度。我们证明该度量可作为混沌性和敏感性的理论代理，而混沌性和敏感性是生成网络类别间判别性动态行为的关键属性。此外，我们引入一种启发式搜索策略，利用锯齿度指导规则选择。实验结果表明，我们的方法达到的分类准确率与全局最优值相差在5%以内，且与穷举方法相比，计算开销降低了90%。我们的发现为优化基于自动机的模式识别方法提供了一种新颖、高效的框架。
英文摘要
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.
