通过执行Networkx triadic_census算法，我可以获得有关每种三重普查类型的节点数的字典
# output from model
op_from_model = <tf.Tensor '1_conv_1x1_parts/BiasAdd:0' shape=(?, 64, 64, 16) dtype=float32>
# Numpy style -
from scipy.ndimage import gaussian_filter, maximum_filter
import numpy as np
lst = np.zeros([16,3])
for i in range(maps.shape[-1]):
_map = maps[:,:,i]
_map = gaussian_filter(_map, sigma=0.3)
_nmsPeaks = non_max_supression(_map, windowSize=3, threshold=1e-6)
y, x = np.where(_nmsPeaks == _nmsPeaks.max())
if len(x) > 0 and len(y) > 0:
lst[:,i] = [int(x[0]), int(y[0]), _nmsPeaks[y[0], x[0]]]
def non_max_supression(map, windowSize, threshold):
under_th_indices = plain < threshold
plain[under_th_indices] = 0
return plain * (plain == maximum_filter(plain, footprint=np.ones((windowSize, windowSize))))
#TF layer style
# adapted from here https://stackoverflow.com/questions/52012657/how-to-make-a-2d-gaussian-filter-in-tensorflow
from keras import backend as K
import tensorflow as tf
def gaussian_kernel(size: int, mean: float, std: float):
d = tf.distributions.Normal(mean, std)
vals = d.prob(tf.range(start = -size, limit = size + 1, dtype = tf.float32))
gauss_kernel = tf.einsum('i,j->ij', vals, vals)
return gauss_kernel / tf.reduce_sum(gauss_kernel)
gauss_kernel = gaussian_kernel(5, 0.44, 0.5) # have to set correct params here
gauss_kernel = gauss_kernel[:, :, tf.newaxis, tf.newaxis]
filt_op = tf.nn.conv2d(np.expand_dims(np.expand_dims(np.array(map[:,:,0] , dtype = np.float32), axis=0),axis=3),
gauss_kernel, strides=[1, 1, 1, 1], padding="SAME")
peaks_nhwc_tensor = tf.nn.max_pool(filt_op, windowSize, strides= [1,1,1,1], padding="SAME", data_format='NHWC')
# not sure of the equivalent op here compared to numpy above
y, x = tf.where(peaks_nhwc_tensor == peaks_nhwc_tensor.max())
现在，我想返回黑社会列表，这些黑社会都遵循人口普查代码“ 201”，“ 120U”或16种现有类型中的任何一种。 如何获得普查计数下的那些节点列表？
答案 0 :(得分：0)
networkx中没有允许您执行的功能，因此应手动实现。我为您修改了networkx.algorithms.triads代码以返回三合会，而不是它们的计数：
import networkx as nx
G = nx.DiGraph()
G.add_nodes_from([1,2,3,4,5])
G.add_edges_from([(1,2),(2,3),(2,4),(4,5)])
triad_census_social=nx.triadic_census(G)
# '003': 2,
# '012': 4,
# '021C': 3,
# '021D': 1,
# another: 0
#: The integer codes representing each type of triad.
#:
#: Triads that are the same up to symmetry have the same code.
TRICODES = (1, 2, 2, 3, 2, 4, 6, 8, 2, 6, 5, 7, 3, 8, 7, 11, 2, 6, 4, 8, 5, 9,
9, 13, 6, 10, 9, 14, 7, 14, 12, 15, 2, 5, 6, 7, 6, 9, 10, 14, 4, 9,
9, 12, 8, 13, 14, 15, 3, 7, 8, 11, 7, 12, 14, 15, 8, 14, 13, 15,
11, 15, 15, 16)
#: The names of each type of triad. The order of the elements is
#: important: it corresponds to the tricodes given in :data:`TRICODES`.
TRIAD_NAMES = ('003', '012', '102', '021D', '021U', '021C', '111D', '111U',
'030T', '030C', '201', '120D', '120U', '120C', '210', '300')
#: A dictionary mapping triad code to triad name.
TRICODE_TO_NAME = {i: TRIAD_NAMES[code - 1] for i, code in enumerate(TRICODES)}
def _tricode(G, v, u, w):
"""Returns the integer code of the given triad.
This is some fancy magic that comes from Batagelj and Mrvar's paper. It
treats each edge joining a pair of `v`, `u`, and `w` as a bit in
the binary representation of an integer.
"""
combos = ((v, u, 1), (u, v, 2), (v, w, 4), (w, v, 8), (u, w, 16),
(w, u, 32))
return sum(x for u, v, x in combos if v in G[u])
census = {name: set([]) for name in TRIAD_NAMES}
n = len(G)
m = {v: i for i, v in enumerate(G)}
for v in G:
vnbrs = set(G.pred[v]) | set(G.succ[v])
for u in vnbrs:
if m[u] <= m[v]:
continue
neighbors = (vnbrs | set(G.succ[u]) | set(G.pred[u])) - {u, v}
# Calculate dyadic triads instead of counting them.
for w in neighbors:
if v in G[u] and u in G[v]:
census['102'].add(tuple(sorted([u, v, w])))
else:
census['012'].add(tuple(sorted([u, v, w])))
# Count connected triads.
for w in neighbors:
if m[u] < m[w] or (m[v] < m[w] < m[u] and
v not in G.pred[w] and
v not in G.succ[w]):
code = _tricode(G, v, u, w)
census[TRICODE_TO_NAME[code]].add(tuple(sorted([u, v, w])))
# null triads, I implemented them manually because the original algorithm computes
# them as _number_of_all_possible_triads_ - _number_of_all_found_triads_
for v in G:
vnbrs = set(G.pred[v]) | set(G.succ[v])
not_vnbrs = set(G.nodes()) - vnbrs
for u in not_vnbrs:
unbrs = set(G.pred[u]) | set(G.succ[u])
not_unbrs = set(G.nodes()) - unbrs
for w in not_unbrs:
wnbrs = set(G.pred[w]) | set(G.succ[w])
if v not in wnbrs and len(set([u, v, w])) == 3:
census['003'].add(tuple(sorted([u, v, w])))
# '003': {(1, 3, 4), (1, 3, 5)},
# '012': {(1, 2, 3), (1, 2, 4), (2, 3, 4), (2, 4, 5)},
# '021C': {(1, 2, 3), (1, 2, 4), (2, 4, 5)},
# '021D': {(2, 3, 4)},
# another: empty
答案 1 :(得分：0)
以vurmux的答案为基础（通过固定“ 102”和“ 012”三元组）：
import networkx as nx
import itertools
def _tricode(G, v, u, w):
"""Returns the integer code of the given triad.
This is some fancy magic that comes from Batagelj and Mrvar's paper. It
treats each edge joining a pair of `v`, `u`, and `w` as a bit in
the binary representation of an integer.
"""
combos = ((v, u, 1), (u, v, 2), (v, w, 4), (w, v, 8), (u, w, 16),
(w, u, 32))
return sum(x for u, v, x in combos if v in G[u])
G = nx.DiGraph()
G.add_nodes_from([1, 2, 3, 4, 5])
G.add_edges_from([(1, 2), (2, 3), (2, 4), (4, 5)])
#: The integer codes representing each type of triad.
#: Triads that are the same up to symmetry have the same code.
TRICODES = (1, 2, 2, 3, 2, 4, 6, 8, 2, 6, 5, 7, 3, 8, 7, 11, 2, 6, 4, 8, 5, 9,
9, 13, 6, 10, 9, 14, 7, 14, 12, 15, 2, 5, 6, 7, 6, 9, 10, 14, 4, 9,
9, 12, 8, 13, 14, 15, 3, 7, 8, 11, 7, 12, 14, 15, 8, 14, 13, 15,
11, 15, 15, 16)
#: The names of each type of triad. The order of the elements is
#: important: it corresponds to the tricodes given in :data:`TRICODES`.
TRIAD_NAMES = ('003', '012', '102', '021D', '021U', '021C', '111D', '111U',
'030T', '030C', '201', '120D', '120U', '120C', '210', '300')
#: A dictionary mapping triad code to triad name.
TRICODE_TO_NAME = {i: TRIAD_NAMES[code - 1] for i, code in enumerate(TRICODES)}
triad_nodes = {name: set([]) for name in TRIAD_NAMES}
m = {v: i for i, v in enumerate(G)}
for v in G:
vnbrs = set(G.pred[v]) | set(G.succ[v])
for u in vnbrs:
if m[u] > m[v]:
unbrs = set(G.pred[u]) | set(G.succ[u])
neighbors = (vnbrs | unbrs) - {u, v}
not_neighbors = set(G.nodes()) - neighbors - {u, v}
# Find dyadic triads
for w in not_neighbors:
if v in G[u] and u in G[v]:
triad_nodes['102'].add(tuple(sorted([u, v, w])))
else:
triad_nodes['012'].add(tuple(sorted([u, v, w])))
for w in neighbors:
if m[u] < m[w] or (m[v] < m[w] < m[u] and
v not in G.pred[w] and
v not in G.succ[w]):
code = _tricode(G, v, u, w)
triad_nodes[TRICODE_TO_NAME[code]].add(
tuple(sorted([u, v, w])))
# find null triads
all_tuples = set()
for s in triad_nodes.values():
all_tuples = all_tuples.union(s)
triad_nodes['003'] = set(itertools.combinations(G.nodes(), 3)).difference(all_tuples)
结果
# print(triad_nodes)
# {'003': {(1, 3, 4), (1, 3, 5)},
# '012': {(1, 2, 5), (1, 4, 5), (2, 3, 5), (3, 4, 5)},
# '102': set(),
# '021D': {(2, 3, 4)},
# '021U': set(),
# '021C': {(1, 2, 3), (1, 2, 4), (2, 4, 5)},
# '111D': set(),
# '111U': set(),
# '030T': set(),
# '030C': set(),
# '201': set(),
# '120D': set(),
# '120U': set(),
# '120C': set(),
# '210': set(),
# '300': set()}
与nx.triadic_census达成协议
# print(nx.triadic_census(G))
# {'003': 2,
# '012': 4,
# '102': 0,
# '021D': 1,
# '021U': 0,
# '021C': 3,
# '111D': 0,
# '111U': 0,
# '030T': 0,
# '030C': 0,
# '201': 0,
# '120D': 0,
# '120U': 0,
# '120C': 0,
# '210': 0,
# '300': 0}