























Abstract:Many complex systems - be they financial, natural, or social - are composed of units - such as stocks, neurons, or agents - whose joint activity can be represented as a multivariate time series. An issue of both practical and theoretical importance concerns the possibility of inferring the presence of a static relationship between any two units solely from their dynamic behaviour. The present contribution aims at tackling such an issue within the framework of traditional hypothesis testing: briefly speaking, our suggestion is that of linking any two units if behaving in a sufficiently similar way. To achieve such a goal, we project a multivariate time series onto a signed graph by i) comparing the empirical properties of the former with those expected under a suitable benchmark and ii) linking any two units with a positive (negative) edge in case the corresponding series shares a significantly large number of concordant (discordant) values. To define our benchmarks, we adopt an information-theoretic approach that is rooted into the constrained maximisation of Shannon entropy, a procedure inducing an ensemble of multivariate time series that preserves some of the empirical properties on average, while randomising everything else. We showcase the possible applications of our method by addressing one of the most timely issues in the domain of neurosciences, i.e. that of determining if brain networks are frustrated or not, and, if so, to what extent. As our results suggest, this is indeed the case, with the major contribution to the underlying negative subgraph coming from the subcortical regions (and, to a lesser extent, from the limbic ones). At the mesoscopic level, the minimisation of the Bayesian Information Criterion, instantiated with the Signed Stochastic Block Model, reveals that brain regions gather into modules aligning with the statistical variant of the Relaxed Balance Theory.
From: Marzio Di Vece [view email]
[v1]
Fri, 1 Aug 2025 11:30:24 UTC (2,398 KB)
[v2]
Tue, 7 Oct 2025 09:11:58 UTC (3,859 KB)
[v3]
Wed, 26 Nov 2025 11:21:56 UTC (1,498 KB)
[v4]
Tue, 26 May 2026 15:40:08 UTC (2,179 KB)
[v5]
Fri, 12 Jun 2026 15:28:45 UTC (2,151 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。