def task2_1():
g = Graph(directed=False)
g = g.Load('./edges.txt', format='edgelist', directed=False)
print("num. edges:", len(g.es()))
print("num. vertices: ", len(g.vs()))
print("diameter: ", g.diameter())
print("transitivity :", g.transitivity_undirected())
print("degree distr. :", g.degree_distribution())
print("degree : ", g.degree())
plot(g, layout = g.layout_kamada_kawai())
prs = g.pagerank();
prs = [prs[i]*500 for i in range(0,len(prs))]
plot(g, vertex_size = prs)
def task2_2():
g = Graph(directed=False)
g = g.Load('edges.txt', format='edgelist', directed=False)
com = g.community_edge_betweenness()
clust = com.as_clustering()
print 'largest community size:', clust.giant().vcount()
com_sizes = map(lambda s: s.vcount(), clust.subgraphs())
plt.hist(com_sizes, bins=max(com_sizes))
plt.title('community size distribution')
plt.show(block=False)
plot(clust, layout=g.layout_kamada_kawai())
def task2_2():
g = Graph(directed=False)
g = g.Load('./edges.txt', format='edgelist', directed=False)
com = g.community_edge_betweenness()
comC = com.as_clustering()
comSizes = comC.sizes()
print("Size of largest community:", max(comSizes))
plt.hist(comSizes, rwidth = 0.5)
plt.ylabel('Number of communities')
plt.xlabel('Community size')
plt.show()
plot(comC, layout = g.layout_kamada_kawai(), orientation='bottom-top')
print ('Clusters:', com.optimal_count)
def task2_1():
g = Graph(directed=False)
g = g.Load('edges.txt', format='edgelist', directed=False)
print '{} {}'.format('#edges:', g.ecount())
print '{} {}'.format('#nodes:', g.vcount())
print '{} {}'.format('diameter:', g.diameter())
print '{} {:.3f}'.format('transitivity', g.transitivity_undirected())
degrees = g.degree(g.vs)
plt.hist(degrees, bins=g.maxdegree())
plt.title('degree distribution')
plt.show(block=False)
weigths = g.pagerank()
g.vs['size'] = map(lambda x: x * 1500, weigths)
plot(g, layout=g.layout_lgl())
def main(argv):
if len(argv) == 0:
sys.exit(0)
try:
opts, args = getopt.getopt(argv, "hi:o:", [])
except getopt.GetoptError:
print(USAGE)
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
print(USAGE)
sys.exit()
elif opt in ('-i'):
input_graph_path = arg
elif opt in ('-o'):
output_path = arg
g = Graph.Load(input_graph_path)
json_g = convert_to_JSON(g)
with open(output_path, 'w') as f:
json.dump(json_g, f)
def show_graph(input_graph_path):
"""
Shows a graph using the PyCairo library. Some default viewing options are currently applied
TODO: Support the configuration of viewing options
:param input_graph_path: The path to the graph to show
"""
g = Graph.Load(input_graph_path)
node_colors = {"COMMIT": "black", "COMMENT": "pink", "ISSUE": "orange", "USERLIKE": "red", "PULL": "blue",
"REPO": "pink"}
commit_nodes = [node.index for node in g.vs if node['type'] == 'COMMIT']
g.delete_vertices(commit_nodes)
graph_style = {"vertex_size": 20,
"vertex_color": [node_colors[node_type] for node_type in g.vs["type"]],
"vertex_label": g.vs["label"],
"edge_width": [1 * edge_flow for edge_flow in g.es['forwardFlow']],
"layout": g.layout("fr"),
"bbox": (1000, 1000),
"margin": 100}
plot(g, **graph_style)
import os
import igraph
from igraph import Graph
import MySQLdb
dbh = MySQLdb.connect(host='xor', db='php_info', user='******')
INFILE = "output/small_jacc_70.net"
OUTFILE_SVG = "out_jax_smaller_100_large.svg"
OUTFILE_PNG = "large_fully_similar.png"
#g = Graph.Load("output/SimpleModuleGraphALL.net")
g = Graph.Load(INFILE)
#g = igraph.load("output/SimpleModuleGraph.net")
#print g.es
#print g.es["weight"]
#print g.cliques()
print len(g.vs)
# delete edges that have low weight
to_del = []
for e in g.es:
# print e
if (e['weight'] < 0.98):
to_del.append(e.index)
g.delete_edges(to_del)
g.simplify()
sub_g = g.subgraph(g.vs.select(_degree_gt=0)).decompose(minelements=20)
nx.write_edgelist(G, f, comments="Node->Node edges", data=False)
f.close()
print("Total time is %.5f sec" % (T.time() - start))
# # read saved graph
# print("Reading graph...")
# start = T.time()
# Gnew = nx.read_edgelist(saved_edges,nodetype=int)
# print("Total time is %.5f sec" %(T.time()-start))
# Using igraph
if graph_driver == 'igraph':
print("\nStarting igraph preprocessing...")
start = T.time()
from igraph import Graph
G0 = Graph.Load(edgelist, format="edgelist")
C = subg = Graph.components(G0, mode="weak")
G = C.subgraph(np.argmax(C.sizes()))
G = G.vs.graph
G = Graph.as_undirected(G)
print(G.vcount(), G.ecount())
# save graph
G.write_graphmlz(datadir + "igraph_graph")
# G.write_edgelist("igraph_edgelist")
# # load graph
# G = Graph.Load("igraph_graph",format="graphmlz")
# # G = Graph.Load("igraph_edgelist",format="edgelist")
# nodes = G.vs.indices
# print(np.amax(nodes) + 1 == G.vcount())
def load(g_path, f_path, meta):
wg = WikiGraph()
wg._page_finder = pickle_load(f_path)
graph_format = meta["graph_format"]
wg.g = Graph.Load(g_path.open("rb"), format=graph_format)
return wg
# use h5py to save and retrieve data
f = h5py.File("D.hdf5", "w", libver='latest')
D = f.create_dataset("D", (nland, nnodes),
data=D_np,
dtype=datatype,
compression="gzip")
print("Total time to fill in D is %.5f sec" % (T.time() - start))
# close
f.close()
if graph_driver == 'igraph':
# load from igraph
G2 = Graph.Load(datadir + "igraph_graph", format="graphmlz")
# G2 = Graph.Load("igraph_edgelist",format="edgelist")
G2 = Graph.as_undirected(G2) # avoid inf when calculating distances
nodes = G2.vs.indices
nnodes = len(nodes)
print("Nodes ids are in order = ", np.amax(nodes) + 1 == G2.vcount())
# get node degrees for each index
degrees = G2.vs.degree()
node_degree_map = dict(zip(nodes, degrees))
ndm_sorted_by_deg = {
k: v
for k, v in sorted(
node_degree_map.items(), key=lambda x: x[1], reverse=True)
}
import os
import igraph
from igraph import Graph
import MySQLdb
import sys
dbh = MySQLdb.connect(host='localhost', db='php_info', user='******')
INFILE = "06attr"
OUTFILE_SVG = "attr_graph_06.svg"
OUTFILE_PNG = "attr_graph_06.png"
g = Graph.Load("06attr.net")
#g = Graph.Read_Ncol(INFILE)
#print g
#print g.is_directed()
#g.to_undirected(False)
#g = g.to_undirected()
#g = Graph.Load("output/SimpleModuleGraphALL.net")
#g = igraph.load("output/SimpleModuleGraph.net")
#print g.es
#print g.es["weight"]
#print g.cliques()
print g.is_directed()
print len(g.vs)
# delete edges that have low weight
