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arXiv 2510.07186cond-mat.stat-mechcond-mat.dis-nnhep-th
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相互作用随机图模型的重整化

Renormalization of Interacting Random Graph Models

Leiden University(莱顿大学);IMT School for Advanced Studies(高等研究IMT学院);Universitá di Palermo(巴勒莫大学)

Alessio Catanzaro, Diego Garlaschelli, Subodh P. Patil

AI总结:

本文研究了相互作用随机图模型的重整化,探讨了闭合形式重整化群变换及其在统计力学中的等价性,提出了在社交网络、意见动态和神经网络中的应用。

摘要:

随机图提供了一种有用的数学表示,用于描述现实世界中的各种复杂网络。例如,指数随机图特别适用于生成具有指定统计矩的随机图。在本研究中,我们探讨了前者的泛化,其中链接概率被其他链接的出现所条件,这对应于在有效的广义统计力学形式化中引入相互作用。当限制在最简单的非平凡情况,即成对相互作用时,可以推导出对应于相应线图的最大协调数为二的闭合形式重整化群变换。更高的协调数不允许可解的闭合形式重整化群变换,这一特征与二维或更多维晶格系统中通常不存在精确变换相呼应。我们引入了无序性,并研究了其对概率分配的诱导重整化群流,突显其与统计流形上有效哈密顿量相关的时间反转各向异性漂移扩散的正式等价性。我们讨论了我们的发现的含义,强调了在随机图中某些类别的成对条件的长波长无关性,并以可能的应用结束。这些包括建模偏好效应在社交网络中的标度行为、意见动态、以及神经网络中的强化效应,以及我们的发现如何提供一个系统框架来处理推断和重建问题中的数据限制。

英文摘要:

Random graphs offer a useful mathematical representation of a variety of real world complex networks. Exponential random graphs, for example, are particularly suited towards generating random graphs constrained to have specified statistical moments. In this investigation, we elaborate on a generalization of the former where link probabilities are conditioned on the appearance of other links, corresponding to the introduction of interactions in an effective generalized statistical mechanical formalism. When restricted to the simplest non-trivial case of pairwise interactions, one can derive a closed form renormalization group transformation for maximum coordination number two on the corresponding line graph. Higher coordination numbers do not admit exact closed form renormalization group transformations, a feature that paraphrases the usual absence of exact transformations in two or more dimensional lattice systems. We introduce disorder and study the induced renormalization group flow on its probability assignments, highlighting its formal equivalence to time reversed anisotropic drift-diffusion on the statistical manifold associated with the effective Hamiltonian. We discuss the implications of our findings, stressing the long wavelength irrelevance of certain classes of pair-wise conditioning on random graphs, and conclude with possible applications. These include modeling the scaling behavior of preferential effects on social networks, opinion dynamics, and reinforcement effects on neural networks, as well as how our findings offer a systematic framework to deal with data limitations in inference and reconstruction problems.

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。

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