Orientation-Sensitive Return Traces in a Four-State Graph Dynamics with Invertible Local Updates

We study a finite-state Markov dynamics on an oriented, weighted, finite simple graph in which each vertex carries one of four local states. After a binary change of coordinates, an active edge update exchanges two occupation variables together with their internal bits and flips one of the transported bits; reversing the edge orientation replaces the local update by its inverse. The occupation number is conserved. Although edge reversal does not change reachability, it can change return traces and hence the spectrum of the transition operator. Our first results locate the earliest possible orientation dependence in terms of the girth. All return traces of order below the girth are orientation-independent. At odd girth the trace at the girth is still independent, whereas at even girth the first possible difference is an explicit weighted sum over shortest cycles with Eulerian-number coefficients. We also derive a closed formula for the next trace order at every even girth, separating repeated-cycle-edge, boundary-edge, and disjoint-edge contributions. In a six-vertex example we determine analytically the first difference of the total trace and obtain an exact coefficient of 7680. A Walsh–Fourier transform in the internal bits gives a complete block decomposition of each fixed-occupation sector. This yields blockwise refinements of the trace formulas and, on even cycles, an arithmetic classification of every fine Fourier block: each block is either orientation-blind for all powers or has an explicitly determined first orientation-sensitive trace order. Exact finite computations are supplied as reproducibility checks, while the stated main results are proved analytically. Preprint. This manuscript has not undergone peer review.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-18
DOI
https://doi.org/10.5281/zenodo.22833913
Primary Topic
Markov Chains and Monte Carlo Methods
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

Orientation-Sensitive Return Traces in a Four-State Graph Dynamics with Invertible Local Updates

Jannis René Becker
Zenodo (CERN European Organization for Nuclear Research)
Markov Chains and Monte Carlo Methods
preprint

Orientation-Sensitive Return Traces in a Four-State Graph Dynamics with Invertible Local Updates

Jannis René Becker
preprint en

Abstract

We study a finite-state Markov dynamics on an oriented, weighted, finite simple graph in which each vertex carries one of four local states. After a binary change of coordinates, an active edge update exchanges two occupation variables together with their internal bits and flips one of the transported bits; reversing the edge orientation replaces the local update by its inverse. The occupation number is conserved. Although edge reversal does not change reachability, it can change return traces and hence the spectrum of the transition operator. Our first results locate the earliest possible orientation dependence in terms of the girth. All return traces of order below the girth are orientation-independent. At odd girth the trace at the girth is still independent, whereas at even girth the first possible difference is an explicit weighted sum over shortest cycles with Eulerian-number coefficients. We also derive a closed formula for the next trace order at every even girth, separating repeated-cycle-edge, boundary-edge, and disjoint-edge contributions. In a six-vertex example we determine analytically the first difference of the total trace and obtain an exact coefficient of 7680. A Walsh–Fourier transform in the internal bits gives a complete block decomposition of each fixed-occupation sector. This yields blockwise refinements of the trace formulas and, on even cycles, an arithmetic classification of every fine Fourier block: each block is either orientation-blind for all powers or has an explicitly determined first orientation-sensitive trace order. Exact finite computations are supplied as reproducibility checks, while the stated main results are proved analytically. Preprint. This manuscript has not undergone peer review.

Zenodo (CERN European Organization for Nuclear Research)
Markov Chains and Monte Carlo Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.