Get the list of Triad nodes , who fall under the category of individual Triadic Census

Question

By executing Networkx triadic_census Algorithm, I\'m able to get the dictionary of the number of nodes falling on each type of triadic census

triad_census_socia         


        

Answer 1

Building on vurmux's answer (by fixing the '102' and '012' triads):

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)

Result

# 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()}

In agreement with nx.triadic_census

# 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}