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🆕 Siblinarity Antichain algorithm
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@GiulioRossetti
GiulioRossetti committed Sep 17, 2020
1 parent 9116f59 commit e054760
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Showing 5 changed files with 836 additions and 44 deletions.
136 changes: 103 additions & 33 deletions cdlib/algorithms/crisp_partition.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,17 @@
try:
import infomap as imp
except ModuleNotFoundError:
imp = None
imp = None

try:
from wurlitzer import pipes
except ModuleNotFoundError:
pipes = None
pipes = None

try:
import igraph as ig
except ModuleNotFoundError:
ig = None
ig = None

try:
import leidenalg
Expand All @@ -23,10 +23,9 @@
except ModuleNotFoundError:
gt = None


from cdlib.algorithms.internal import DER
import community as louvain_modularity

import warnings
from collections import defaultdict
from cdlib import NodeClustering, FuzzyNodeClustering
from cdlib.algorithms.internal.em import EM_nx
Expand All @@ -35,6 +34,7 @@
from cdlib.algorithms.internal.AGDL import Agdl
from cdlib.algorithms.internal.FuzzyCom import fuzzy_comm
from cdlib.algorithms.internal.Markov import markov
from cdlib.algorithms.internal.SiblinarityAntichain import matrix_node_recursive_antichain_partition
from karateclub import EdMot
import markov_clustering as mc
from chinese_whispers import chinese_whispers as cw
Expand All @@ -47,7 +47,7 @@
__all__ = ["louvain", "leiden", "rb_pots", "rber_pots", "cpm", "significance_communities", "surprise_communities",
"greedy_modularity", "der", "label_propagation", "async_fluid", "infomap", "walktrap", "girvan_newman", "em",
"scan", "gdmp2", "spinglass", "eigenvector", "agdl", "frc_fgsn", "sbm_dl", "sbm_dl_nested",
"markov_clustering", "edmot", "chinesewhispers"]
"markov_clustering", "edmot", "chinesewhispers", "siblinarity_antichain"]


def girvan_newman(g_original, level):
Expand Down Expand Up @@ -153,7 +153,7 @@ def scan(g_original, epsilon, mu):
algorithm = SCAN_nx(g, epsilon, mu)
coms = algorithm.execute()
return NodeClustering(coms, g_original, "SCAN", method_parameters={"epsilon": epsilon,
"mu": mu})
"mu": mu})


def gdmp2(g_original, min_threshold=0.75):
Expand Down Expand Up @@ -256,7 +256,7 @@ def eigenvector(g_original):

communities = [g.vs[x]['name'] for x in coms]

return NodeClustering(communities, g_original, "Eigenvector", method_parameters={"":""})
return NodeClustering(communities, g_original, "Eigenvector", method_parameters={"": ""})


def agdl(g_original, number_communities, number_neighbors, kc, a):
Expand Down Expand Up @@ -294,8 +294,8 @@ def agdl(g_original, number_communities, number_neighbors, kc, a):
coms.append([nodes[n] for n in com])

return NodeClustering(coms, g_original, "AGDL", method_parameters={"number_communities": number_communities,
"number_neighbors": number_neighbors,
"kc": kc, "a": a})
"number_neighbors": number_neighbors,
"kc": kc, "a": a})


def louvain(g_original, weight='weight', resolution=1., randomize=False):
Expand Down Expand Up @@ -339,8 +339,9 @@ def louvain(g_original, weight='weight', resolution=1., randomize=False):
coms_to_node[c].append(n)

coms_louvain = [list(c) for c in coms_to_node.values()]
return NodeClustering(coms_louvain, g_original, "Louvain", method_parameters={"weight": weight, "resolution": resolution,
"randomize": randomize})
return NodeClustering(coms_louvain, g_original, "Louvain",
method_parameters={"weight": weight, "resolution": resolution,
"randomize": randomize})


def leiden(g_original, initial_membership=None, weights=None):
Expand Down Expand Up @@ -381,7 +382,7 @@ def leiden(g_original, initial_membership=None, weights=None):
)
coms = [g.vs[x]['name'] for x in part]
return NodeClustering(coms, g_original, "Leiden", method_parameters={"initial_membership": initial_membership,
"weights": weights})
"weights": weights})


def rb_pots(g_original, initial_membership=None, weights=None, resolution_parameter=1):
Expand Down Expand Up @@ -430,8 +431,8 @@ def rb_pots(g_original, initial_membership=None, weights=None, resolution_parame
initial_membership=initial_membership, weights=weights)
coms = [g.vs[x]['name'] for x in part]
return NodeClustering(coms, g_original, "RB Pots", method_parameters={"initial_membership": initial_membership,
"weights": weights,
"resolution_parameter": resolution_parameter})
"weights": weights,
"resolution_parameter": resolution_parameter})


def rber_pots(g_original, initial_membership=None, weights=None, node_sizes=None, resolution_parameter=1):
Expand Down Expand Up @@ -478,8 +479,9 @@ def rber_pots(g_original, initial_membership=None, weights=None, node_sizes=None
)
coms = [g.vs[x]['name'] for x in part]
return NodeClustering(coms, g_original, "RBER Pots", method_parameters={"initial_membership": initial_membership,
"weights": weights, "node_sizes": node_sizes,
"resolution_parameter": resolution_parameter})
"weights": weights,
"node_sizes": node_sizes,
"resolution_parameter": resolution_parameter})


def cpm(g_original, initial_membership=None, weights=None, node_sizes=None, resolution_parameter=1):
Expand Down Expand Up @@ -534,8 +536,8 @@ def cpm(g_original, initial_membership=None, weights=None, node_sizes=None, reso
weights=weights, node_sizes=node_sizes, )
coms = [g.vs[x]['name'] for x in part]
return NodeClustering(coms, g_original, "CPM", method_parameters={"initial_membership": initial_membership,
"weights": weights, "node_sizes": node_sizes,
"resolution_parameter": resolution_parameter})
"weights": weights, "node_sizes": node_sizes,
"resolution_parameter": resolution_parameter})


def significance_communities(g_original, initial_membership=None, node_sizes=None):
Expand Down Expand Up @@ -579,7 +581,7 @@ def significance_communities(g_original, initial_membership=None, node_sizes=Non
node_sizes=node_sizes)
coms = [g.vs[x]['name'] for x in part]
return NodeClustering(coms, g_original, "Significance", method_parameters={"initial_membership": initial_membership,
"node_sizes": node_sizes})
"node_sizes": node_sizes})


def surprise_communities(g_original, initial_membership=None, weights=None, node_sizes=None):
Expand Down Expand Up @@ -626,7 +628,8 @@ def surprise_communities(g_original, initial_membership=None, weights=None, node
weights=weights, node_sizes=node_sizes)
coms = [g.vs[x]['name'] for x in part]
return NodeClustering(coms, g_original, "Surprise", method_parameters={"initial_membership": initial_membership,
"weights": weights, "node_sizes": node_sizes})
"weights": weights,
"node_sizes": node_sizes})


def greedy_modularity(g_original, weight=None):
Expand Down Expand Up @@ -741,7 +744,7 @@ def walktrap(g_original):
for c in coms:
communities.append([g.vs[x]['name'] for x in c])

return NodeClustering(communities, g_original, "Walktrap", method_parameters={"":""})
return NodeClustering(communities, g_original, "Walktrap", method_parameters={"": ""})


def label_propagation(g_original):
Expand Down Expand Up @@ -774,7 +777,7 @@ def label_propagation(g_original):
coms = list(nx.algorithms.community.label_propagation_communities(g))
coms = [list(x) for x in coms]

return NodeClustering(coms, g_original, "Label Propagation", method_parameters={"":""})
return NodeClustering(coms, g_original, "Label Propagation", method_parameters={"": ""})


def async_fluid(g_original, k):
Expand Down Expand Up @@ -846,7 +849,7 @@ def der(g_original, walk_len=3, threshold=.00001, iter_bound=50):
coms.append([maps[n] for n in c])

return NodeClustering(coms, g_original, "DER", method_parameters={"walk_len": walk_len, "threshold": threshold,
"iter_bound": iter_bound})
"iter_bound": iter_bound})


def frc_fgsn(g_original, theta, eps, r):
Expand Down Expand Up @@ -894,10 +897,10 @@ def frc_fgsn(g_original, theta, eps, r):
coms = [list(c) for c in communities]

return FuzzyNodeClustering(coms, fuzz_assoc, g_original, "FuzzyComm", method_parameters={"theta": theta,
"eps": eps, "r": r})
"eps": eps, "r": r})


def sbm_dl(g_original, B_min=None,B_max=None, deg_corr=True, **kwargs):
def sbm_dl(g_original, B_min=None, B_max=None, deg_corr=True, **kwargs):
"""Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models.
Fit a non-overlapping stochastic block model (SBM) by minimizing its description length using an agglomerative heuristic.
Expand Down Expand Up @@ -936,11 +939,12 @@ def sbm_dl(g_original, B_min=None,B_max=None, deg_corr=True, **kwargs):
affiliations = state.get_blocks().get_array()
affiliations = {label_map[i]: affiliations[i] for i in range(len(affiliations))}
coms = affiliations2nodesets(affiliations)
coms = [list(v) for k,v in coms.items()]
return NodeClustering(coms, g_original, "SBM", method_parameters={"B_min": B_min, "B_max": B_max, "deg_corr": deg_corr})
coms = [list(v) for k, v in coms.items()]
return NodeClustering(coms, g_original, "SBM",
method_parameters={"B_min": B_min, "B_max": B_max, "deg_corr": deg_corr})


def sbm_dl_nested(g_original, B_min=None,B_max=None, deg_corr=True, **kwargs):
def sbm_dl_nested(g_original, B_min=None, B_max=None, deg_corr=True, **kwargs):
"""Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. (nested)
Fit a nested non-overlapping stochastic block model (SBM) by minimizing its description length using an agglomerative heuristic.
Expand Down Expand Up @@ -981,8 +985,9 @@ def sbm_dl_nested(g_original, B_min=None,B_max=None, deg_corr=True, **kwargs):
affiliations = level0.get_blocks().get_array()
affiliations = {label_map[i]: affiliations[i] for i in range(len(affiliations))}
coms = affiliations2nodesets(affiliations)
coms = [list(v) for k,v in coms.items()]
return NodeClustering(coms, g_original, "SBM_nested", method_parameters={"B_min": B_min, "B_max": B_max, "deg_corr": deg_corr})
coms = [list(v) for k, v in coms.items()]
return NodeClustering(coms, g_original, "SBM_nested",
method_parameters={"B_min": B_min, "B_max": B_max, "deg_corr": deg_corr})


def markov_clustering(g_original, expansion=2, inflation=2, loop_value=1, iterations=100, pruning_threshold=0.001,
Expand Down Expand Up @@ -1129,4 +1134,69 @@ def edmot(g_original, component_count=2, cutoff=10):

coms = [list(c) for c in coms_to_node.values()]

return NodeClustering(coms, g_original, "EdMot", method_parameters={"component_count": component_count, "cutoff": cutoff})
return NodeClustering(coms, g_original, "EdMot",
method_parameters={"component_count": component_count, "cutoff": cutoff})


def siblinarity_antichain(g_original, forwards_backwards_on=True, backwards_forwards_on=False,
Lambda=1, with_replacement=False, space_label=None, time_label=None):
"""
The algorithm extract communities from a DAG that (i) respects its intrinsic order and (ii) are composed of similar nodes.
The approach takes inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns.
:param g_original: a networkx/igraph object representing a DAG (directed acyclic graph)
:param forwards_backwards_on: checks successors' similarity. Boolean, default True
:param backwards_forwards_on: checks predecessors' similarity. Boolean, default True
:param Lambda: desired resolution of the partition. Default 1
:param with_replacement: If True he similarity of a node to itself is equal to the number of its neighbours based on which the similarity is defined. Boolean, default True.
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.siblinarity_antichain(G, Lambda=1)
:References:
Vasiliauskaite, V., Evans, T.S. Making communities show respect for order. Appl Netw Sci 5, 15 (2020). https://doi.org/10.1007/s41109-020-00255-5
.. note:: Reference implementation: https://github.com/vv2246/siblinarity_antichains
"""

g = convert_graph_formats(g_original, nx.Graph)

if not nx.is_directed_acyclic_graph(g):
raise Exception("The Siblinarity Antichain algorithm require as input a Directed Acyclic Graph (DAG).")

with warnings.catch_warnings():
warnings.simplefilter("ignore")
result_list = matrix_node_recursive_antichain_partition(g, forwards_backwards_on=forwards_backwards_on,
backwards_forwards_on=backwards_forwards_on,
Q_check_on=True,
Lambda=Lambda, with_replacement=with_replacement,
space_label=None, time_label=None)

node_partition = {}
for n in g.nodes():
p_at_level = result_list[0]["n_to_p"][n]
for i in range(1, len(result_list) - 1):
p_at_level = result_list[i]["n_to_p"][p_at_level]
node_partition[n] = p_at_level

partition = defaultdict(list)
for key, val in node_partition.items():
partition[val].append(key)

coms = [list(c) for c in partition.values()]


return NodeClustering(coms, g_original, "Siblinarity Antichain",
method_parameters={"forwards_backwards_on": forwards_backwards_on,
"backwards_forwards_on": backwards_forwards_on,

"Lambda": Lambda,
"with_replacement": with_replacement,
"space_label": space_label,
"time_label": time_label})
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