The Series So Far

IPhenomenon
IIStructure
IIITaxonomy
IVRules

Paper I established the phenomenon: coherent dynamics on the 600-cell that are absent in control graphs. Paper II identified the structure: seven algebraic invariants governing the nine-sector spectrum. Paper III classified the taxonomy: four attractor classes with a non-generic distribution.

Paper IV asks the next question: are there rules governing which attractors can form? Not just whether structure exists, but which specific configurations are allowed and which are forbidden.

The Mode-Coupling Constraint Matrix

The central result is an empirical mode-coupling matrix constructed from 1,165 stable attractors across 1,200 simulated trajectories. For each stable configuration, the paper records which of the nine spectral sectors are active (carrying >10% of the mode energy) and which co-occur.

21 Observed pairings
15 Absent pairings
2.6× Backbone vs departure
97% Stable trajectories
Key Result

Of 36 possible off-diagonal sector pairings, only 21 are observed in stable attractors. The remaining 15 are absent — not because they weren't sampled, but because the spectral organisation of the H₄ graph prevents those combinations from forming persistent configurations.

9x9 mode coupling matrix showing which spectral sector pairings occur in stable attractors
The empirical mode-coupling matrix. Gold cells indicate frequently co-occurring sector pairings. Crosses mark absent pairings. The green box highlights the spectral backbone (S1–S6). Coupling is concentrated in backbone sectors; departure sectors participate only through specific channels.

Six Empirical Constraints

The coupling analysis reveals six reproducible constraints governing the attractor space:

C1

Mode selectivity. Stable attractors are not formed from arbitrary combinations of eigenmodes. Only a restricted subset of sector pairings occurs with appreciable frequency.

C2

Backbone dominance. The spectral backbone (sectors S1–S6, comprising 91 of 120 modes) accounts for the dominant share of attractor composition. Backbone sectors activate at 2.6× the rate of departure sectors.

C3

Localisation threshold. Spatially localised breather-like attractors emerge above a nonlinearity threshold (β ≳ 0.1) and concentrate energy in the inner distance shells of the graph.

C4

Stability–composition correlation. Attractor persistence correlates with spectral composition: backbone-dominated configurations are more temporally stable than those involving departure sectors.

C5

Attractor finiteness. The number of distinct attractor families is small relative to the combinatorial space of possible sector combinations.

C6

Symmetry-related sets. Attractors related by the H₄ symmetry group occur as equivalent sets under vertex permutation. The coupling matrix inherits the conjugate pairing structure of the spectrum.


Where Energy Lives

The 600-cell graph has palindromic distance shells: (1, 12, 32, 42, 32, 1). Paper IV shows that the spatial distribution of energy within attractors is not uniform but reflects this geometry.

Localised breather states concentrate 34% of their energy on just 13 vertices (the centroid and its 12 neighbours) — 11% of the graph carrying a third of the energy. Backbone harmonic states distribute energy more uniformly across the middle shells.

Shell energy distribution per attractor class showing inner-shell concentration for breathers
Mean shell energy fraction per attractor class. Breather states (Class 3) concentrate energy in inner shells; backbone modes (Class 1) distribute closer to uniform. Dashed lines show the uniform reference.

A Stability Hierarchy

Not all attractor classes are equally stable. The paper identifies a clear hierarchy:

  • Class 1 (backbone harmonic) — highest temporal persistence (Cmax = 0.764), lowest localisation. 96.7% backbone spectral energy.
  • Class 2 (locked multi-mode) — intermediate persistence (Cmax = 0.578). Most prevalent class across all β.
  • Class 3 (breather) — lower persistence (Cmax = 0.520) but 6× stronger spatial localisation. A trade-off between temporal persistence and spatial concentration.
Stability metrics per attractor class showing trade-off between persistence and localisation
Stability hierarchy: trajectory count, temporal persistence (peak autocorrelation), and spatial localisation (mean IPR) per attractor class.

Controlled Comparison

The same analysis pipeline applied to degree-matched control graphs (random 12-regular and degree-preserving rewired 600-cell) produces dramatically different results:

  • Control graphs produce ~5 stable attractors per graph vs 1,165 on the 600-cell
  • No coherent coupling structure emerges
  • Destroying the H₄ symmetry by rewiring eliminates the structured coupling pattern entirely
Side-by-side coupling matrices for H4 graph vs random control graph
Mode coupling matrices: H₄ graph (left) vs random 12-regular control (right). The H₄ matrix is sparse and structured; the control produces too few stable attractors to form any coherent pattern.

What This Does Not Claim

  • That the selection rules are analytically derived — they are empirical observations from numerical simulation
  • That every possible attractor has been sampled — the ensemble is finite (1,200 trajectories)
  • That absent pairings are exactly forbidden — they may be suppressed below the detection threshold
  • Any direct mapping to physical particles, forces, or quantum selection rules

The term "selection rule" is used in the dynamical sense — an empirically observed restriction on which persistent configurations can form — not in the quantum-mechanical sense of a symmetry-derived exact prohibition.


The geometry does not just permit structure — it selects which structures are allowed.

Summary of selection rules showing observed vs absent pairings and backbone vs departure activation
Summary: 21 of 36 possible sector pairings observed; backbone sectors activate at 2.6× the rate of departure sectors.
Full paper and code are open-access. All simulations, classification pipelines, and coupling analysis available at github.com/vfd-org/vfd-h4-spectral-geometry.