复杂生态和生物网络结构稳定性的决定因素：Google矩阵方法生物和生态网络稳定性的决定因素：Google矩阵方法
Determinants of Structural Stability in Complex Ecological and Biological Networks: the Google Matrix Approach Determinants of stability in biological and ecological networks: the Google matrix approach
摘要：
梅斯在70年代的理论工作中证明了复杂性导致生物系统的不稳定性，从而与既定的范式相矛盾。在这里，梅斯随机矩阵建模方法被推广到现实的大规模网络的物种相互作用，无论他们是由竞争网络，互利共生或两者兼而有之。简单的关系被发现来管理这些原本棘手的模型，并控制生物系统是稳定和可行的参数范围。我们的模型和真实的经验网络的分析是唯一实现后，引入一个简化的谷歌矩阵的减少计划，在这个过程中，产生一个实用的生态特征值稳定性指标。这些结果提供了一个网络拓扑结构，特别是连通性，如何影响物种稳定共存的理解。在这些系统中，控制可行性（正平衡种群）的约束比控制稳定性的约束更具限制性，这有助于解释为什么许多类可行的生态模型几乎总是稳定的。
英文摘要：
Mays celebrated theoretical work of the 70s contradicted the established paradigm by demonstrating that complexity leads to instability in biological systems. Here Mays random-matrix modelling approach is generalized to realistic large-scale webs of species interactions, be they structured by networks of competition, mutualism or both. Simple relationships are found to govern these otherwise intractable models, and control the parameter ranges for which biological systems are stable and feasible. Our analysis of model and real empirical networks is only achievable upon introducing a simplifying Google-matrix reduction scheme, which in the process, yields a practical ecological eigenvalue stability index. These results provide an understanding on how network topology, especially connectance, influences species stable coexistence. Constraints controlling feasibility (positive equilibrium populations) in these systems, are found more restrictive than those controlling stability, helping explain the enigma of why many classes of feasible ecological models are nearly always stable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
