def multiplex_configuration_independent(mg,seed=None):
"""Run configuration model independently for each layer.
Parameters
----------
mg : Multinet
Multiplex network to be configured.
seed : object
Seed for the configuration model.
Return
------
A new Multinet instance.
"""
layers = mg.layers()
nl = len(layers)
seeds = _random_int_list(nl,seed=seed)
nmg = Multinet()
for layer in layers:
sg = mg.sub_layer(layer,remove_isolates=True)
nodes = sg.in_degree().keys()
in_degs = sg.in_degree().values()
out_degs = sg.out_degree().values()
rsg = nx.directed_configuration_model(in_degs,out_degs,create_using=nx.DiGraph(),seed=seeds.pop())
rnodes = rsg.nodes()
mapping = dict(zip(rnodes,nodes))
nrsg = nx.relabel_nodes(rsg,mapping)
nmg.add_layer(nrsg,layer)
return nmg
def multiplex_erdos_renyi(mg,seed=None,include_all=True):
"""Return a Multinet such that each layer is an Erdos-Renyi network with same p as the original Multinet given.
Parameters
----------
mg : Multinet
Multiplex network to be configured.
seed : object
Seed for the model.
Return
------
A new Multinet instance.
"""
layers = mg.layers()
nl = len(layers)
seeds = _random_int_list(nl,seed=seed)
nmg = Multinet()
for layer in layers:
sg = mg.sub_layer(layer,remove_isolates=not include_all)
nodes = sg.nodes()
nnode = sg.number_of_nodes()
nedge = sg.number_of_edges()
p = float(nedge)/pow(nnode,2)
rsg = nx.erdos_renyi_graph(nnode,p,seed=seeds.pop(),directed=True)
rnodes = rsg.nodes()
mapping = dict(zip(rnodes,nodes))
nrsg = nx.relabel_nodes(rsg,mapping)
nmg.add_layer(nrsg,layer)
return nmg
def reconstruct_from_bipartite(bg):
"""Reconstruct a multiplex network from a layer-edge bipartite graph.
Parameters:
-----------
bg: nx.Graph
A layer-edge bipartite graph that used to reconstruct the multiplex network.
"""
top,bottom = bipartite_sets(bg)
mg = Multinet()
layers = map(_remove_prefix,list(top))
for nodes in bottom:
mg.add_nodes_from(nodes)
u = nodes[0]
v = nodes[1]
for layer in bg[nodes]:
layer = _remove_prefix(layer)
mg.add_edge(u,v,layer)
return mg
def multinet_from_csv(
file_name,
filter_func=util.default_filter,
weight_func=util.default_weight,
layer_func=util.default_layer,
ow='ORIGIN',dw='DEST',
**csv_reader_argv):
"""Build Multinet from csv files.
Parameters:
-----------
filename: str
The path to csv file.
filter_func: func
A function takes line description and a line from the csv file and return a bool. See util/filter_helper for detail.
weight_func: func or str
A function takes line description and a line from the csv file and return a number. See util/weight_helper for detail.
layer_func: func or str
A function takes line description and a line from the csv file and return a str. See util/layer_helper for detail.
ow, dw: str
The key to get nodes name.
csv_reader_argv:
Arguments for internal csv reader. If not specified, use default settings. See Python csv module for detail.
"""
index_dict = {}
mg = Multinet()
if type(weight_func) == str:
weight_func = util.weight_from_string(weight_func)
if type(layer_func) == str:
layer_func = util.layer_from_string(layer_func)
with open(file_name) as netfile:
if not csv_reader_argv:
netreader = csv.reader(netfile,delimiter=',',quotechar='\"',quoting=csv.QUOTE_NONNUMERIC)
else:
netreader = csv.reader(netfile,**csv_reader_argv)
index_line = netreader.next()
index = 0
for item in index_line:
index_dict[item] = index
index += 1
origin_index = index_dict[ow]
dest_index = index_dict[dw]
for line in netreader:
if not filter_func(index_dict,line):
continue
origin = line[origin_index]
dest = line[dest_index]
layer = layer_func(index_dict,line)
weight = weight_func(index_dict,line)
mg.add_node(origin)
mg.add_node(dest)
mg.aggregate_edge(origin,dest,layer,weight)
return mg
