Wenjia Rao | Searcharxiv

arXiv2026

Simplicial closure fragments the explosive cooperation transitions in higher-order public goods games

Hypergraph (HG) and simplicial complex (SC) are two common representations of higher-order networks, and are often expected to differ mainly quantitatively when they encode comparable group interactions. Here we show that this expectation fails in evolutionary cooperation dynamics. Using a controlled higher-order public goods game (PGG) with minimal ad hoc parameters, we compare cooperation transitions on randomized HG and SC constructed from the same triangular backbone. We find that the impact of simplicial closure is selective: when the cooperation transition is continuous-like on HG, imposing simplicial closure mainly broadens the transition without changing its qualitative nature; however, when the transition is explosive and first-order-like on HG, simplicial closure will fragment the compact low/high bistability into a broad ensemble of metastable final states. Detailed analysis reveals that this selective effect arises from the dual role of simplicial closure in cooperative-nucleus dynamics: it promotes the survival of local cooperative nuclei while suppressing their conversion into system-wide cascades. Further experiments confirm that this fragmentation is not tied to a specific payoff form, but is a generic feature of explosive cooperation transitions in higher-order PGGs.

physics.soc-ph↗

arXiv2024

A Complex Network Analysis on The Eigenvalue Spectra of Random Spin Systems

Recent works have established a novel viewpoint that treats the eigenvalue spectra of disordered quantum systems as time-series, and corresponding algorithms such as singular-value-decomposition has proven its advantage in studying subtle physical quantities like Thouless energy and non-ergodic extended regime. On the other hand, algorithms from complex networks have long been known as a powerful tool to study highly nonlinear time-series. In this work, we combine these two ideas together. Using the particular algorithm called visibility graph (VG) that transforms the eigenvalue spectra of a random spin system into complex networks, it's shown the degree distribution of the resulting network is capable of signaturing the eigenvalue evolution during the thermal to many-body localization transition, and the networks in the thermal phase have a small-world structure. We further show these results are robust even when the eigenvalues are incomplete with missing levels, which reveals the advantage of the VG algorithm.

cond-mat.dis-nn↗