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另一种针对过程的方法利用对称性定义的等价类来构造粗粒化方案。在从生物科学到计算机科学的研究中，能够表征网络上动态过程局部对称性的方法为刻画系统基本模块提供了普适性原则<ref name="Leifer_Symmetry_driven_reconstruction">{{cite journal|author1=Leifer I|author2=Phillips D|author3=Sorrentino F|author4=Makse H. A.|title=Symmetry-driven network reconstruction through pseudobalanced coloring optimization|journal=J. Stat. Mech.|year=2022|url=}}</ref>。具体而言，当网络在节点变换下能够保持输入树不变（input trees invariant）时，网络会涌现均衡划分（equitable partitions）<ref name="Boldi_Fibrations_graphs">{{cite journal|author1=Boldi P|author2=Vigna S.|title=Fibrations of graphs|journal=Discrete Math.|year=2002|volume=243|url=}}</ref>，该性质已被用于定义常微分方程的成块性（lumpability）<ref name="Cardelli_Maximal_aggregation">{{cite journal|author1=Cardelli L|author2=Tribastone M|author3=Tschaikowski M|author4=Vandin A.|title=Maximal aggregation of polynomial dynamical systems|journal=Proc. Natl Acad. Sci. USA|year=2017|volume=114|url=}}</ref><ref name="Cardelli_ERODE">{{cite journal|author1=Cardelli L|author2=Tribastone M|author3=Tschaikowski M|author4=Vandin A.|title=Erode: a tool for the evaluation and reduction of ordinary differential equations|journal=Proc. Tools and Algorithms for the Construction and Analysis of Systems|year=2017|pages=310–328|url=}}</ref>、布尔后向等价（Boolean backward equivalence）<ref name="Argyris_Reducing_Boolean_networks">{{cite journal|author1=Argyris G. A.|author2=Lluch Lafuente A|author3=Tribastone M|author4=Tschaikowski M|author5=Vandin A.|title=Reducing Boolean networks with backward equivalence|journal=BMC Bioinformatics|year=2023|volume=24|url=}}</ref>以及纤维染色性质（fibration colouring）<ref name="Morone_Fibration_symmetries">{{cite journal|author1=Morone F|author2=Leifer I|author3=Makse H. A.|title=Fibration symmetries uncover the building blocks of biological networks|journal=Proc. Natl Acad. Sci. USA|year=2020|volume=117|url=}}</ref>等概念。
另一种针对过程的方法利用对称性定义的等价类来构造粗粒化方案。在从生物科学到计算机科学的研究中，能够表征网络上动态过程局部对称性的方法为刻画系统基本模块提供了普适性原则<ref name="Leifer_Symmetry_driven_reconstruction">{{cite journal|author1=Leifer I|author2=Phillips D|author3=Sorrentino F|author4=Makse H. A.|title=Symmetry-driven network reconstruction through pseudobalanced coloring optimization|journal=J. Stat. Mech.|year=2022|url=}}</ref>。具体而言，当网络在节点变换下能够保持输入树不变（input trees invariant）时，网络会涌现均衡划分（equitable partitions）<ref name="Boldi_Fibrations_graphs">{{cite journal|author1=Boldi P|author2=Vigna S.|title=Fibrations of graphs|journal=Discrete Math.|year=2002|volume=243|url=}}</ref>，该性质已被用于定义常微分方程的成块性（lumpability）<ref name="Cardelli_Maximal_aggregation">{{cite journal|author1=Cardelli L|author2=Tribastone M|author3=Tschaikowski M|author4=Vandin A.|title=Maximal aggregation of polynomial dynamical systems|journal=Proc. Natl Acad. Sci. USA|year=2017|volume=114|url=}}</ref><ref name="Cardelli_ERODE">{{cite journal|author1=Cardelli L|author2=Tribastone M|author3=Tschaikowski M|author4=Vandin A.|title=Erode: a tool for the evaluation and reduction of ordinary differential equations|journal=Proc. Tools and Algorithms for the Construction and Analysis of Systems|year=2017|pages=310–328|url=}}</ref>、布尔后向等价（Boolean backward equivalence）<ref name="Argyris_Reducing_Boolean_networks">{{cite journal|author1=Argyris G. A.|author2=Lluch Lafuente A|author3=Tribastone M|author4=Tschaikowski M|author5=Vandin A.|title=Reducing Boolean networks with backward equivalence|journal=BMC Bioinformatics|year=2023|volume=24|url=}}</ref>以及纤维染色性质（fibration colouring）<ref name="Morone_Fibration_symmetries">{{cite journal|author1=Morone F|author2=Leifer I|author3=Makse H. A.|title=Fibration symmetries uncover the building blocks of biological networks|journal=Proc. Natl Acad. Sci. USA|year=2020|volume=117|url=}}</ref>等概念。
对称性诱导的均衡划分可将系统约化为保留整体动力学与同步特性的动力学变量的集合，此特性充分证明了该方法的实用性<ref name="Gili_Fibration_symmetry_breaking">{{cite journal|author1=Gili T|author2=et al.|title=Fibration symmetry-breaking supports functional transitions in a brain network engaged in language|journal=Preprint at arXiv|year=2024|url=}}</ref>。尽管基于对称性的方法识别针对过程的等价类节点这一重要网络约化技术极为有效，但其通常仅能确定单一划分（通常对应最大网络约化）。与纯拓扑学方法类似，这类方法无法迭代应用（注：无法迭代是因为经过一步约化后，对称性或拓扑性质就发生了改变。），且在对称性缺失的体系中缺乏普适性。
对称性诱导的均衡划分可将系统约化为保留整体动力学与同步特性的动力学变量的集合，此特性充分证明了该方法的实用性<ref name="Gili_Fibration_symmetry_breaking">{{cite journal|author1=Gili T|author2=et al.|title=Fibration symmetry-breaking supports functional transitions in a brain network engaged in language|journal=Preprint at arXiv|year=2024|url=}}</ref>。尽管基于对称性的方法识别针对过程的等价类节点这一重要网络约化技术极为有效，但其通常仅能确定单一划分（通常对应最大网络约化）。与纯拓扑学方法类似，这类方法无法迭代应用（注：无法迭代是因为经过一步约化后，对称性或拓扑性质就发生了改变。），且在对称性缺失的体系中缺乏普适性。