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Feature Extraction
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csv
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Tags:
graph-theory
graph-isomorphism
color-refinement
weisfeiler-lehman
structural-invariants
graph-kernels
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76fecdf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | """Color refinement (WL) y utilidades — autocontenido.
- wl_sym: WL simétrico (in/out por color, grados crudos) — el del teorema.
- wl_k_colors: k-FWL (folklore, correlacionada; ≡ (k+1)-WL) — la jerarquía
de observadores. El nombre "k-WL" a secas designa la variante estándar
(multiséts separados por posición), que es estrictamente más débil.
- helpers de grafos (bitmasks), complemento, digrafos.
"""
import hashlib
from collections import Counter
from itertools import product
def h(s):
return hashlib.sha256(s.encode()).hexdigest()[:12]
def wl_sym(n, adj, f, h_fn=None, info=False, rondas_fijas=None):
"""Perfil del WL simétrico: multiset de tamaños de clase.
Por defecto corre hasta que la partición se estabiliza (tope n+2).
`h_fn` permite cambiar la función de hash (certificado de colisión);
`rondas_fijas` fuerza un número exacto de rondas (certificado de
convergencia); `info=True` devuelve (perfil, rondas_usadas).
"""
hh = h_fn or h
in_adj = transp(n, adj)
colors = [hh(f"g|{adj[v].bit_count()}|{in_adj[v].bit_count()}")
for v in range(n)]
prev = None
usadas = 0
tope = rondas_fijas if rondas_fijas is not None else (n + 2)
for r in range(tope):
new = []
for v in range(n):
cnt = Counter()
m = adj[v]
while m:
u = (m & -m).bit_length() - 1
m &= m - 1
cnt[colors[u]] += 1
ci = Counter()
m = in_adj[v]
while m:
u = (m & -m).bit_length() - 1
m &= m - 1
ci[colors[u]] += 1
keys = set(cnt) | set(ci)
prof = tuple(sorted((c, f(ci.get(c, 0)), f(cnt.get(c, 0)))
for c in keys))
new.append(hh(f"{colors[v]}|{prof}"))
colors = new
usadas = r + 1
if rondas_fijas is None:
part = tuple(sorted(Counter(colors).values()))
if part == prev:
break
prev = part
perfil = tuple(sorted(Counter(colors).values()))
return (perfil, usadas) if info else perfil
def transp(n, adj):
out = [0] * n
for v in range(n):
m = adj[v]
while m:
u = (m & -m).bit_length() - 1
m &= m - 1
out[u] |= 1 << v
return out
def wl_k_colors(n, adj, k, rounds=None):
"""k-FWL (folklore, correlacionada; ≡ (k+1)-WL): colores de k-tuplas.
La actualización usa pares de posiciones (i,j) con el MISMO w; por la
equivalencia conocida es tan fuerte como (k+1)-WL. La variante estándar
("k-WL", multiséts separados) es más débil y NO separa Rook de
Shrikhande en k=3 (ver EXP-159 del laboratorio)."""
if rounds is None:
rounds = n + 2
tuplas = list(product(range(n), repeat=k))
def init(t):
grados = [str(adj[a].bit_count()) for a in t]
est = []
for i in range(k):
for j in range(i + 1, k):
a, b = t[i], t[j]
est.append("=" if a == b else
("1" if (adj[a] >> b) & 1 else "0"))
return h("|".join(grados) + ":" + "".join(est))
colors = {t: init(t) for t in tuplas}
prev = None
for _ in range(rounds):
new = {}
for t in tuplas:
vecinos = []
for i in range(k):
for j in range(i + 1, k):
for w in range(n):
t2 = t[:i] + (w,) + t[i + 1:]
t3 = t[:j] + (w,) + t[j + 1:]
vecinos.append((i, j, colors[t2], colors[t3]))
vecinos.sort()
new[t] = h(colors[t] + "|" + ",".join(
f"{i}{j}:{a}:{b}" for i, j, a, b in vecinos))
colors = new
firma = tuple(sorted(colors.values()))
if firma == prev:
break
prev = firma
return colors
def wl_k_union(adj1, adj2, k):
n1, n2 = len(adj1), len(adj2)
n = n1 + n2
adj_u = [0] * n
for v in range(n1):
adj_u[v] = adj1[v]
for v in range(n2):
adj_u[n1 + v] = adj2[v] << n1
return wl_k_colors(n, adj_u, k, rounds=n + 2)
def separados(adj1, adj2, k):
"""¿k-FWL separa los dos grafos? (colores compartidos en la unión)."""
n1 = len(adj1)
colors = wl_k_union(adj1, adj2, k)
c1 = Counter(colors[t] for t in product(range(n1), repeat=k))
c2 = Counter(colors[t] for t in product(range(n1, n1 + len(adj2)),
repeat=k))
return set(c1) != set(c2) or sorted(c1.values()) != sorted(c2.values())
def build_adj(n, mask):
adj = [0] * n
bit = 0
for i in range(n):
for j in range(i + 1, n):
if (mask >> bit) & 1:
adj[i] |= 1 << j
adj[j] |= 1 << i
bit += 1
return adj
def comp_adj(n, adj):
out = [0] * n
for i in range(n):
for j in range(i + 1, n):
if not (adj[i] >> j) & 1:
out[i] |= 1 << j
out[j] |= 1 << i
return out
def all_digraphs(n):
pos = [(u, v) for u in range(n) for v in range(n) if u != v]
for mask in range(1 << len(pos)):
adj = [0] * n
for k, (u, v) in enumerate(pos):
if (mask >> k) & 1:
adj[u] |= 1 << v
yield mask, adj
def all_graphs(n):
for mask in range(1 << (n * (n - 1) // 2)):
yield mask, build_adj(n, mask)
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