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Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
Latched Hyperprior Hypothesis (LHH) — a sustained vascular contraction engages the smooth-muscle latch-bridge, durably freezing a circuit as a committed hyperprior isolated from global updating; unlatches when its prediction resolves. Use when modeling durable commitments, trauma/PTSD as cemented priors, latch spirals, or the cross-substrate latch (mechanical/bioelectric/immune/sheaf).
Synaptic substrate of biological active inference — long-term potentiation/depression (LTP/LTD) writes priors into synaptic weights = the learning landscape (Deep CANALs). A held vascular latch annealed long enough crystallizes into a neuron prior. Use when modeling consolidation, neuron priors, the inference→learning landscape write-path, or commit-to-disk of a held prediction.
基于 SOC 职业分类
正在显示 SKILL.md
| name | ihara-zeta |
| description | Ihara zeta function for graphs: non-backtracking walks, prime cycles, and spectral analysis via det(I - uB). |
| source | music-topos + graph theory |
| license | MIT |
| trit | 0 |
| bundle | spectral |
| xenomodern | true |
| ironic_detachment | 0.27 |
"The Ihara zeta function encodes all non-backtracking closed walks - the 'prime cycles' of a graph."
The Ihara zeta function generalizes the Riemann zeta function to graphs:
For a graph G, the Ihara zeta function is:
ζ_G(u) = ∏_{[C]} (1 - u^{|C|})^{-1}
where:
A walk v₀ → v₁ → v₂ → ... → vₖ is non-backtracking if:
vᵢ₊₁ ≠ vᵢ₋₁ for all i
(Never immediately return to the previous vertex)
ζ_G(u)^{-1} = (1 - u²)^{|E| - |V|} · det(I - uB)
where B is the non-backtracking matrix.
Indexed by directed edges (e, f) where head(e) = tail(f) and e ≠ f⁻¹:
function non_backtracking_matrix(G)
# Directed edges: 2|E| entries
directed_edges = [(u,v) for (u,v) in edges(G)
for dir in [(u,v), (v,u)]]
m = length(directed_edges)
B = zeros(m, m)
for (i, e) in enumerate(directed_edges)
for (j, f) in enumerate(directed_edges)
# e = (a→b), f = (c→d)
# Connect if b = c AND a ≠ d (non-backtracking)
if e[2] == f[1] && e[1] != f[2]
B[i, j] = 1
end
end
end
return B
end
| Number Theory | Graph Theory |
|---|---|
| Prime number p | Prime cycle C |
| log p | Length |
| Riemann zeta ζ(s) | Ihara zeta ζ_G(u) |
| Prime Number Theorem | Cycle counting asymptotics |
| Riemann Hypothesis | Ramanujan property |
A path of length n is prime (non-backtracking) iff μ(n) ≠ 0:
function is_prime_path(path)
"""
Check if path is non-backtracking (prime).
Equivalent to μ(length) ≠ 0 in our encoding.
"""
for i in 2:length(path)-1
if path[i-1] == path[i+1]
return false # Backtracking detected
end
end
return true
end
function moebius_filter(paths)
"""
Filter to prime (non-backtracking) paths using Möbius.
μ(n) ≠ 0 ⟺ n is squarefree ⟺ no repeated factors ⟺ no backtracking.
"""
return filter(is_prime_path, paths)
end
A d-regular graph G satisfies the Graph Riemann Hypothesis if all poles of ζ_G(u) in |u| < 1/√(d-1) lie on the circle |u| = 1/√(d-1).
Theorem: G is Ramanujan ⟺ G satisfies the Graph Riemann Hypothesis.
function check_graph_riemann_hypothesis(G)
d = degree(G)
B = non_backtracking_matrix(G)
# Eigenvalues of B
eigenvalues = eigvals(B)
# Poles of zeta at 1/λ for each eigenvalue λ
poles = 1 ./ eigenvalues
# Check: all poles with |u| < 1/√(d-1) lie on |u| = 1/√(d-1)
critical_radius = 1 / √(d - 1)
for pole in poles
r = abs(pole)
if r < critical_radius && abs(r - critical_radius) > 0.001
return false # Pole inside critical circle but not on it
end
end
return true
end
Bordenave-Lelarge-Massoulié (2015):
Non-backtracking spectral clustering succeeds down to the information-theoretic threshold, where adjacency-based methods fail.
function non_backtracking_clustering(G, k)
"""
Cluster graph into k communities using non-backtracking eigenvectors.
Succeeds where spectral clustering on adjacency matrix fails
(the 'spectral redemption' phenomenon).
"""
B = non_backtracking_matrix(G)
# Get top k+1 eigenvectors (skip trivial)
λ, V = eigen(B)
idx = sortperm(abs.(λ), rev=true)
# Project directed edge eigenvectors to vertices
vertex_embeddings = project_to_vertices(G, V[:, idx[2:k+1]])
# Cluster in embedding space
return kmeans(vertex_embeddings, k)
end
function ihara_zeta_coefficient(G, n)
"""
Coefficient of u^n in log ζ_G(u).
= (1/n) × (number of primitive closed non-backtracking walks of length n)
"""
B = non_backtracking_matrix(G)
# tr(B^n) counts all closed non-backtracking walks of length n
# Möbius inversion extracts primitive ones
total = tr(B^n)
# Subtract non-primitive (powers of shorter cycles)
primitive_count = 0
for d in divisors(n)
if d < n
primitive_count += moebius(n ÷ d) * ihara_zeta_coefficient(G, d) * d
end
end
return (total - primitive_count) / n
end
function ihara_zeta_inverse(G, u)
"""
Compute ζ_G(u)^{-1} using Bass-Hashimoto formula.
"""
B = non_backtracking_matrix(G)
n_vertices = nv(G)
n_edges = ne(G)
# ζ_G(u)^{-1} = (1 - u²)^{|E| - |V|} × det(I - uB)
return (1 - u^2)^(n_edges - n_vertices) * det(I - u * B)
end
| Component | Trit | Role |
|---|---|---|
| ramanujan-expander | -1 | Validator - spectral bounds |
| ihara-zeta | 0 | Coordinator - non-backtracking structure |
| moebius-inversion | +1 | Generator - alternating sums |
Conservation: (-1) + (0) + (+1) = 0 ✓
Ihara Zeta (Graphs)
/\
/ \
/ \
/ \
Möbius -------- Chromatic
(Number Theory) (Combinatorics)
All three connect via:
CREATE TABLE prime_cycles (
cycle_id VARCHAR PRIMARY KEY,
graph_id VARCHAR,
vertices VARCHAR[],
length INT,
is_primitive BOOLEAN,
equivalence_class INT,
seed BIGINT
);
CREATE TABLE zeta_coefficients (
graph_id VARCHAR,
n INT,
coefficient FLOAT,
primitive_count INT,
computed_at TIMESTAMP,
PRIMARY KEY (graph_id, n)
);
CREATE TABLE non_backtracking_spectrum (
graph_id VARCHAR PRIMARY KEY,
eigenvalues FLOAT[],
spectral_radius FLOAT,
satisfies_grh BOOLEAN, -- Graph Riemann Hypothesis
is_ramanujan BOOLEAN
);
just ihara-zeta graph.json # Compute zeta function
just ihara-primes graph.json 10 # List prime cycles up to length 10
just ihara-grh graph.json # Check Graph Riemann Hypothesis
just ihara-cluster graph.json 3 # Non-backtracking clustering
just ihara-spectrum graph.json # Eigenvalues of B matrix
ramanujan-expander - Spectral gap and Alon-Boppanamoebius-inversion - Alternating sums, prime extractionthree-match - Graph coloring (chromatic polynomial)acsets - Graph representation