def xnet2graphml(filename, outfilename='/tmp/mygraph.graphml'):
"""
Convert xnet object to a .graphml format
Parameters
----------
g : xnet graph
Returns
-------
strOut : string
String that can be written to a .xnet file.
"""
g = xnet2igraph(filename)
if not 'x' in g.vertex_attributes() and 'posx' in g.vertex_attributes():
g.vs['x'] = g.vs['posx']; del g.vs['posx']
if not 'y' in g.vertex_attributes() and 'posy' in g.vertex_attributes():
g.vs['y'] = g.vs['posy']; del g.vs['posy']
for attr in g.vertex_attributes():
if type(g.vs[0][attr]) != float: continue
g.vs[attr] = np.array(g.vs[attr]).astype(str).tolist()
import igraph
igraph.write(g, outfilename)
def main():
parser = argparse.ArgumentParser(description="Convert an igraph to a csc object")
parser.add_argument("graph_fn", action="store", help="The name of the igraph to read from disk. *Must be gml format!")
parser.add_argument("-g", "--gen_graph", action="store_true", help="Generate a new ER graph")
parser.add_argument("-n", "--num_nodes", action="store", type=int, help="The number of nodes in the ER graph")
parser.add_argument("-p", "--probability", action="store", type=float, help="The probability of connectivity of each node to another in the graph")
parser.add_argument("-s", "--save", action="store_true", help="Save conversion to disk")
parser.add_argument("-f", "--save_fn", action="store", default="csc_matlab", help="Save file name")
parser.add_argument("-t", "--test", action="store_true", help="Run test only!")
result = parser.parse_args()
if result.test:
test()
exit(1)
if os.path.exists(result.graph_fn):
g = igraph.read(result.graph_fn, format="gml")
elif result.gen_graph or result.num_nodes or result.probability:
assert (result.gen_graph and result.num_nodes and result.probability), "You must set all ER parameters i.e. n, p"
g = igraph.Graph.Erdos_Renyi(n=result.num_nodes, p=result.probability)
igraph.write(g, result.save_fn+".gml", format="gml")
else:
sys.stderr.writelines("Invalid path %s ... and all (i.e. n, p, g) ER parameters not set so no action taken. \n EXITING NOW! \n")
exit(-1)
igraph_to_csc(g, result.save, result.save_fn)
def gml2core(gmlfolder, svgfolder):
fw = open(svgoutpath + 'vecount.txt', 'w')
svgtype = [
'_weakGaint', '_strongGaint', '_weakGaintSPT', '_strongGaintSPT'
]
if os.path.exists(svgoutpath) == 0:
os.mkdir(svgoutpath)
for file in os.listdir(gmlinpath):
if os.path.splitext(file)[1] == '.gml':
print 'Reading graph from ...', file
try:
gmlfile = open(gmlinpath + file)
g = ig.Graph.Read_GML(gmlfile)
gg = clus.VertexClustering.giant(g.clusters(mode='weak'))
print g.vcount(), gg.vcount()
# es=ig.EdgeSeq(gg)
vs = ig.VertexSeq(gg)
for v in vs:
indeg = v.indegree()
outdeg = v.outdegree()
if indeg < 1 or outdeg < 1:
gg.delete_vertices(v)
# if outdeg<1:
# gg.delete_vertices(v)
print gg.vcount()
ig.write(gg, svgoutpath + file + svgtype[1] + '.gml')
# ig.Graph.write_svg(gg, svgoutpath+file+svgtype[1]+'.svg', layout='large')
except Exception, e:
print gmlinpath + file, ' failed', e
pass
gmlfile.close()
def main():
# Head of output console
print("-----------------------------------------------------")
print("-------------------Program Start---------------------")
print("-----------------------------------------------------")
# Record time
startTime = time.time()
# Get args
args = get_args(sys.argv[1:], "CSV to GML script")
# Set input and output steam
filename_in = args.filename_in
filename_out = args.filename_out
# Import data
toleratio = args.toleratio
data, data_trimmed = readin(filename_in, toleratio)
print("Data imported.")
# Get the names
dimension_names = [data[1:]] #unused in this program
node_names = [vector[0] for vector in data][1:] #remove 'gene_id'
# Get adjacency matrix
print("-----------------------------------------------------")
print("Computing adj_matrix...")
sim_meas = args.sim_meas
edge_ratio = args.edge_ratio
print("Similarity measure: " + sim_meas)
adjMatrix = getAdjacency(data_trimmed, sim_meas, edge_ratio)
print("Done.")
# Now we can convert adjacency matrix to graph
vertices = node_names
edges = []
n = len(adjMatrix)
for i in range(0, n):
for j in range(0, n):
if adjMatrix[i][j] == 1:
edge = tuple([i, j])
edges.insert(len(edges), edge)
print("-----------------------------------------------------")
print("Start Generating Graph...")
g = Graph(vertex_attrs={"label": vertices}, edges=edges, directed=False)
print("Graph Generated. There are " + str(len(vertices)) +
" vertices and " + str(len(edges)) + " edges.")
# Export to gml file
print("-----------------------------------------------------")
print("Start Writing to GML file...")
ig.write(g, format="gml", filename=filename_out)
print("Writing Completed. Output file is named " + filename_out)
# Tail of output console
# Report time
print("Finished in %0.4f seconds" % (time.time() - startTime))
print("-----------------------------------------------------")
print("------------------Program Finish---------------------")
print("-----------------------------------------------------")
def write(self, filename):
'''
Save the graph into gramml format
'''
#startBudgetYear = int(str(budgetYears[0]))
#endBudgetYear = int(str(budgetYears[-1]))
#filename = '%s/result/%d-%d.%s'%(root_folder(),startBudgetYear, endBudgetYear, format) if extra == None else '%s/result/%d-%d.%s.%s'%(root_folder(),startBudgetYear, endBudgetYear, extra,format)
igraph.write(self.g, filename, format='graphml')
def write(self, filename):
'''
Save the graph into gramml format
'''
#startBudgetYear = int(str(budgetYears[0]))
#endBudgetYear = int(str(budgetYears[-1]))
#filename = '%s/result/%d-%d.%s'%(root_folder(),startBudgetYear, endBudgetYear, format) if extra == None else '%s/result/%d-%d.%s.%s'%(root_folder(),startBudgetYear, endBudgetYear, extra,format)
igraph.write(self.g, filename, format='graphml')
def multi_run(node_list, run_time, model_type):
i = 0
graph_list = []
while i < run_time:
if model_type == 4:
IG = model_4(nodes)
elif model_type == 5:
IG = model_5(nodes)
graph_list.append(IG)
i += 1
ig.write(
graph_list[0], r'G:\HFS\WeiboData\HFSWeiboGML\test\simulation_' +
str(model_type) + '.gml')
return graph_list
def main() -> None:
parser = cli_parser()
args = parser.parse_args()
output_path: Path = args.output
if output_path is None:
output_path = Path('./model.graphml')
log.info(f'Using default output path {output_path.absolute()}')
else:
if output_path.exists():
log.warning(f'Will overwrite file {output_path.absolute()}')
if output_path.is_dir():
raise RuntimeError(f'Specified output path {output_path.absolute()} is a directory.')
network = model.generate(vars(args))
if output_path.is_file():
log.warning(f'Overwriting file {output_path}')
with open(output_path.absolute(), 'w') as f:
ig.write(network, f, format='graphml')
def do_experiment(node_file_path, gml_file_path, runtime):
name = os.path.split(os.path.basename(gml_file_path))[0]
nodes = read_list_nodes_from_file(node_file_path, 2)
BA_list = multi_run(nodes, 30, 5, name)
FAN_list = multi_run(nodes, 30, 4, name)
RG = real_data(gml_file_path)
ig.write(
BA_list[0],
r'G:\HFS\WeiboData\HFSWeiboGML\test\simulation_BA_' + name + '.gml')
ig.write(
FAN_list[0],
r'G:\HFS\WeiboData\HFSWeiboGML\test\simulation_Fans_' + name + '.gml')
ig.write(RG, r'G:\HFS\WeiboData\HFSWeiboGML\test\real_' + name + '.gml')
def convert_graph(linein, lineout):
datastruct = np.load(linein)
#the attribute where the timeseries is stored
data=datastruct['roi_data']
numrois=data.shape[0]
cormtx=np.corrcoef(data)
cormtx=abs(cormtx)
#subtract out the diagonal
cormtx=(cormtx-numpy.eye(numrois))
#initialize empty dictionary of floats
edge_dict=defaultdict(float)
#add element wise to a dictionary of the edges
for i in range(0, numrois) :
#print("Processing roi {0}...".format(i))
for j in range(0, numrois) :
edge_dict[(i, j)] = cormtx[i,j];
#print("Adding edges to the graph...")
new_graph = ig.Graph(n=numrois, directed=False)
new_graph+=edge_dict.keys()
#print("Adding edge weights to the graph...")
new_graph.es["weight"] = edge_dict.values()
z=ig.write(new_graph, lineout, format="graphml")
visual_style['edge_width'] = [10*float(w) \
for w in graph.es['weight']]
ig.plot(graph, filename, **visual_style)
plot(tg)
#%% EDGES
print('NO DIRIGIDO')
for idx, e in enumerate(tg2.es):
print(idx, e.tuple, e['weight'])
print('DIRIGIDO')
for idx, e in enumerate(tg.es):
print(idx, e.tuple, e['weight'])
#%% VERTEX
print('NO DIRIGIDO')
for idx, v in enumerate(tgs2.vs):
print(idx, v.index, v.degree(), v['label'])
#print('DIRIGIDO')
#for idx, v in enumerate(tg.vs):
# print(idx, v.index, v['label'])
#%%
ig.write(tg2, 'test-undirected.gml')
def real_data(file_path):
name = os.path.split(os.path.basename(file_path))[0]
IG = ig.Graph.Read_GML(file_path)
IG = IG.subgraph_edges(IG.es.select(plzftype_in=['2', '8']))
ig.write(IG, r'G:\HFS\WeiboData\HFSWeiboGML\test\real_' + name + '.gml')
return IG
def SR(): nodeAttrFolder = r'G:\HFS\WeiboData\HFSWeiboNodeData' gmlFolder = r'G:\HFS\WeiboData\HFSWeiboGML' mentionlist = wt.csv2list_new(csvfilepath=r"G:\HFS\WeiboData\HFSWeiboStatNet\Stat\Mentioncntlist.txt") meta = wt.csv2list(r'G:\HFS\WeiboData\Statistics\meta_successed308.txt') metadic = wt.list2dict(meta) print metadic netAttr_R = [] netAttr_S = [] indegree_R = [] indegree_S = [] outdegree_R = [] outdegree_S = [] netAttr_R_writer = csv.writer(file(r'G:\HFS\WeiboData\HFSWeiboStatNet\RS\netAttr_R.csv','w')) netAttr_S_writer = csv.writer(file(r'G:\HFS\WeiboData\HFSWeiboStatNet\RS\netAttr_S.csv','w')) indegree_R_writer = csv.writer(file(r'G:\HFS\WeiboData\HFSWeiboStatNet\RS\indegree_R.csv','w')) indegree_S_writer = csv.writer(file(r'G:\HFS\WeiboData\HFSWeiboStatNet\RS\indegree_S.csv','w')) outdegree_R_writer = csv.writer(file(r'G:\HFS\WeiboData\HFSWeiboStatNet\RS\outdegree_R.csv','w')) outdegree_S_writer = csv.writer(file(r'G:\HFS\WeiboData\HFSWeiboStatNet\RS\outdegree_S.csv','w')) errorlist = [] i = 0 for filef in os.listdir(gmlFolder): # try: i+=1 if i<3000: filename = os.path.splitext(filef)[0] forwordcnt = 10000000 try: forwordcnt = metadic.get(str(filename))[4] except: pass if float(forwordcnt)<2001: print i, filename,time.clock() IG_r=real_data(gmlFolder+'\\'+filename+'.gml')#real_data(r"G:\MyPapers\CMO\testData4Cui\3343740313561521.gml") "two steps nwtwork growth, one by one " nodes = read_list_nodes_from_file(nodeAttrFolder+'\\'+filename+'.repost', start_line=2) IG_s=model_4(nodes) mention_list = find_mention_list_from_file(mentionlist, filename) IG_s=mention_model_1(IG_s, mention_list, len(IG_r.vs)) gmlf_s = gmlFolder+'\\SimulationGml\\sim'+filename+'.gml' ig.write(IG_s,gmlf_s) # IG_r=clus.VertexClustering.giant(IG_r.clusters(mode='weak')) ra = analysisNet(IG_r)#(ig.VertexClustering.giant(IG_r.clusters(mode='weak'))) sa = analysisNet(IG_s) ra.insert(0,filename) sa.insert(0,filename) netAttr_R.append(ra) netAttr_S.append(sa) rIndegree = IG_r.indegree() sIndegree = IG_s.indegree() rOutdegree = IG_r.outdegree() sOutdegree = IG_s.outdegree() rIndegree.insert(0,filename) sIndegree.insert(0,filename) rOutdegree.insert(0,filename) sOutdegree.insert(0,filename) indegree_R.append(rIndegree) indegree_S.append(sIndegree) outdegree_R.append(rOutdegree) outdegree_S.append(sOutdegree) netAttr_R_writer.writerow(ra) netAttr_S_writer.writerow(sa) indegree_R_writer.writerow(rIndegree) indegree_S_writer.writerow(sIndegree) outdegree_R_writer.writerow(rOutdegree) outdegree_S_writer.writerow(sOutdegree) else: pass # except: # errorlist.append(filef) # print 'error==========================:',filef print 'all the errors==========================:',len(errorlist),errorlist
def save_graph(self, graph_path):
write(self.graph, graph_path)
mention_list = read_mention_list_from_file(r"G:\HFS\WeiboData\HFSWeiboStatNet\Stat\test\Mentioncntlist.txt", 3) print "mention list================" print len(mention_list) print sum(mention_list) nodes = read_list_nodes_from_file(r"G:\HFS\WeiboData\HFSWeiboNodeData\3343740313561521.repost", 2) IG_s=model_4(nodes) print "withou mention ================" print len(IG_s.vs) print len(IG_s.es) IG_s=mention_model_1(IG_s, mention_list, len(IG_r.vs)) print "with mention ================" print len(IG_s.vs) print len(IG_s.es) gmlf_s = 'temp.gml' ig.write(IG_s,gmlf_s) print IG_r.vs.attributes() ss = IG_s.vs.attributes() print ss # for v in IG_s.vs: # print v['label'] #wt.list_2_Distribution(IG_s.indegree(), IG_r.indegree()) #degree_list = multi_run(nodes, 1, 5) #all_degree=[] #for degree in degree_list:
def export_graph(graph, filename):
"""Saves graph in GML format"""
print("Exporting...")
igraph.write(graph, filename, format="graphmlz")
print("Exported file to {}".format(filename))
def main():
parser = argparse.ArgumentParser(
description="Convert an igraph to a csc object")
parser.add_argument(
"graph_fn",
action="store",
help="The name of the igraph to read from disk. *Must be gml format!")
parser.add_argument("-g",
"--gen_graph",
action="store_true",
help="Generate a new ER graph")
parser.add_argument("-n",
"--num_nodes",
action="store",
type=int,
help="The number of nodes in the ER graph")
parser.add_argument(
"-p",
"--probability",
action="store",
type=float,
help=
"The probability of connectivity of each node to another in the graph")
parser.add_argument("-s",
"--save",
action="store_true",
help="Save conversion to disk")
parser.add_argument("-f",
"--save_fn",
action="store",
default="csc_matlab",
help="Save file name")
parser.add_argument("-t",
"--test",
action="store_true",
help="Run test only!")
result = parser.parse_args()
if result.test:
test()
exit(1)
if os.path.exists(result.graph_fn):
g = igraph.read(result.graph_fn, format="gml")
elif result.gen_graph or result.num_nodes or result.probability:
assert (result.gen_graph and result.num_nodes and
result.probability), "You must set all ER parameters i.e. n, p"
g = igraph.Graph.Erdos_Renyi(n=result.num_nodes, p=result.probability)
igraph.write(g, result.save_fn + ".gml", format="gml")
else:
sys.stderr.writelines(
"Invalid path %s ... and all (i.e. n, p, g) ER parameters not set so no action taken. \n EXITING NOW! \n"
)
exit(-1)
igraph_to_csc(g, result.save, result.save_fn)
def write_graph(self, G, dest_filename):
ig.write(G, dest_filename, format="graphml")
#igraph.summary(c.Graph)
#c.make_Graph(15, 200, prob = 0.1)
#igraph.summary(c.Graph)
#print len(c.clique_list)
#g = c.Graph.copy()
#d = DataPolishing(g)
#igraph.summary(d.Graph)
#print len(d.Graph.maximal_cliques(min = 3))
#igraph.write(d.Graph, "randam_clique_5000.gml")
#d.data_polish(polish_ratio = pr)
#igraph.summary(d.Graph)
#print len(d.Graph.maximal_cliques(min = 3))
#igraph.write(d.Graph, "polished_clique_5000.gml")
#print "recall = " , c.recall(d.Graph)
#print "precision = " , c.precision(d.Graph)
#print "accuracy = " , c.accuracy(d.Graph)
g = igraph.read("twitter_graph.gml")
a = DataPolishing(g)
print "original:"
igraph.summary(a.Graph)
print len(a.Graph.maximal_cliques(min=3))
a.data_polish(polish_ratio=pr)
print "polished"
igraph.summary(a.Graph)
print len(a.Graph.maximal_cliques(min=3))
igraph.write(a.Graph, "polished_twitter_grapht.gml")
if item["name"] in actors and item["name"] not in dic.keys():
roles.append("a")
#g.vs["role"][counter] = "a"
elif item["name"] in dic.keys() and item["name"] not in actors:
#g.vs["role"][counter] = "d"
roles.append("d")
else:
roles.append("b")
g.vs["role"] = roles
color_dict = {"a": "red", "d": "blue", "b": "green"}
g.vs["color"] = [color_dict[role] for role in g.vs["role"]]
# if eid >= 0:
# print(eid)
# g.es[eid]["weight"] += 1
# else:
# g.add_edge(i, j, weight=1)
#
#
#
# pl=gr.plot(g);
# pl.add(g);
# pl._windows_hacks=True;
# pl.show()
gr.write(
g,
"E:/Related to Univesity/Arshad/term 1/Network Science(Teimourpour)/exercise4(project)/graph2.gml"
)
def save_cell_graph(self, filename='cell_graph.gml'):
"""Save the cell to cell connectivity graph in a GML file.
"""
igraph.write(self.__cell_graph, filename, format='gml')
print 'Saved cell-to-cell connectivity data in', filename
def analyze_json(worker):
"""
Take in a set of json community detection results files and a graphml file representing the raw graph and output a
graphml file that contains, as attributes, the results of the algorithms
Args:
worker: Named tuple of json_path raw_graph_path output_path timeout
"""
signal.signal(signal.SIGALRM, __handle_timeout)
signal.setitimer(signal.ITIMER_REAL, worker.timeout)
print('Loading raw Graphml file truth file: %s'%worker.raw_graph_path)
if worker.raw_graph_path is not None:
G = igraph.load(worker.raw_graph_path)
else:
print("ERROR: Not able to load graph")
return
try:
for json_path in worker.json_path:
with open(json_path) as f:
data = json.load(f)
(name, algorithm) = data['job_name'].split('--')[:2]
algo_name = 'algo_%s'%algorithm
# Only if we are pulling least frequent
if worker.pick_least_frequent or worker.pick_most_frequent:
# Calculate number of nodes in each community
community_counts = {}
for node in data['membership']:
for community in node:
if community in community_counts:
community_counts[community] += 1
else:
community_counts[community] = 1
# Add property to graph
for node in G.vs():
# Get cover Array
# TODO: Fix this hacky way to turn node id (i.e. "n1") into node index (i.e. 1)
try:
community_array = data['membership'][int(node['id'][1:])]
except IndexError:
community_array= []
if worker.pick_least_frequent:
least_frequent_community = __get_least_frequent_community(community_array, community_counts, reverse=False)
if least_frequent_community is None:
least_frequent_community = -1
G.vs[node.index][algo_name] = str(least_frequent_community)
elif worker.pick_most_frequent:
least_frequent_community = __get_least_frequent_community(community_array, community_counts, reverse=True)
if least_frequent_community is None:
least_frequent_community = -1
G.vs[node.index][algo_name] = str(least_frequent_community)
else:
G.vs[node.index][algo_name] = ','.join([str(x) for x in community_array])
except TimeoutError as t:
print("\t+Timeout ERROR: was analyzing: ", data['job_name'])
signal.alarm(0)
return
except Exception as e:
print(e)
traceback.print_exc(file=sys.stdout)
return
graphml_file_output = os.path.join(worker.output_path, "%s.graphml"% name)
print("Writing Graph: %s"%graphml_file_output )
igraph.write(G, graphml_file_output)
#igraph.summary(c.Graph)
#c.make_Graph(15, 200, prob = 0.1)
#igraph.summary(c.Graph)
#print len(c.clique_list)
#g = c.Graph.copy()
#d = DataPolishing(g)
#igraph.summary(d.Graph)
#print len(d.Graph.maximal_cliques(min = 3))
#igraph.write(d.Graph, "randam_clique_5000.gml")
#d.data_polish(polish_ratio = pr)
#igraph.summary(d.Graph)
#print len(d.Graph.maximal_cliques(min = 3))
#igraph.write(d.Graph, "polished_clique_5000.gml")
#print "recall = " , c.recall(d.Graph)
#print "precision = " , c.precision(d.Graph)
#print "accuracy = " , c.accuracy(d.Graph)
g = igraph.read("twitter_graph.gml")
a = DataPolishing(g)
print "original:"
igraph.summary(a.Graph)
print len(a.Graph.maximal_cliques(min = 3))
a.data_polish(polish_ratio = pr)
print "polished"
igraph.summary(a.Graph)
print len(a.Graph.maximal_cliques(min = 3))
igraph.write(a.Graph, "polished_twitter_grapht.gml")
def analyze_json(worker):
"""
Take in a set of json community detection results files and a graphml file representing the raw graph and output a
graphml file that contains, as attributes, the results of the algorithms
Args:
worker: Named tuple of json_path raw_graph_path output_path timeout
"""
signal.signal(signal.SIGALRM, __handle_timeout)
signal.setitimer(signal.ITIMER_REAL, worker.timeout)
print('Loading raw Graphml file truth file: %s' % worker.raw_graph_path)
if worker.raw_graph_path is not None:
G = igraph.load(worker.raw_graph_path)
else:
print("ERROR: Not able to load graph")
return
try:
for json_path in worker.json_path:
with open(json_path) as f:
data = json.load(f)
(name, algorithm) = data['job_name'].split('--')[:2]
algo_name = 'algo_%s' % algorithm
# Only if we are pulling least frequent
if worker.pick_least_frequent or worker.pick_most_frequent:
# Calculate number of nodes in each community
community_counts = {}
for node in data['membership']:
for community in node:
if community in community_counts:
community_counts[community] += 1
else:
community_counts[community] = 1
# Add property to graph
for node in G.vs():
# Get cover Array
# TODO: Fix this hacky way to turn node id (i.e. "n1") into node index (i.e. 1)
try:
community_array = data['membership'][int(
node['id'][1:])]
except IndexError:
community_array = []
if worker.pick_least_frequent:
least_frequent_community = __get_least_frequent_community(
community_array, community_counts, reverse=False)
if least_frequent_community is None:
least_frequent_community = -1
G.vs[node.index][algo_name] = str(
least_frequent_community)
elif worker.pick_most_frequent:
least_frequent_community = __get_least_frequent_community(
community_array, community_counts, reverse=True)
if least_frequent_community is None:
least_frequent_community = -1
G.vs[node.index][algo_name] = str(
least_frequent_community)
else:
G.vs[node.index][algo_name] = ','.join(
[str(x) for x in community_array])
except TimeoutError as t:
print("\t+Timeout ERROR: was analyzing: ", data['job_name'])
signal.alarm(0)
return
except Exception as e:
print(e)
traceback.print_exc(file=sys.stdout)
return
graphml_file_output = os.path.join(worker.output_path, "%s.graphml" % name)
print("Writing Graph: %s" % graphml_file_output)
igraph.write(G, graphml_file_output)
#%%
## EDGES
idxi = 0
idxf = 7
print('NO DIRIGIDO')
for idx, e in enumerate(g2.es[idxi:idxf]):
print(idx, e.tuple, e['weight'])
print('DIRIGIDO')
for idx, e in enumerate(g.es[idxi:idxf]):
print(idx, e.tuple, e['weight'])
#%%
'''
GUARDAR
'''
<<<<<<< HEAD
ig.write(g,'forks-directed.gml')
ig.write(g2, 'users-undirected.gml')
=======
#ig.write(g,'users-directed.gml')
ig.write(rcg, 'repos-CG-undirected.gml')
>>>>>>> 223870946b8243d9fcad6eb1e1904594f77d1ea7
#! /usr/bin/python
import json
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from networkx.readwrite import json_graph
import igraph
g = igraph.read("weighted_karate.gml")
h = g.community_leading_eigenvector(clusters=2)
[a,b] = h.subgraphs();
igraph.write(a, "a", format="gml");
igraph.write(b, "b", format="gml");
c = nx.read_gml("a");
data = json_graph.node_link_data(c);
with open('c.json', 'w') as f:
json.dump(data, f, indent=4);
d = nx.read_gml("b");
data = json_graph.node_link_data(d);
with open('d.json', 'w') as f:
json.dump(data, f, indent=4);
#with open('rgraph.json', 'w') as f:
# json.dump(data, f, indent=4)
