def saveGraph(graph):
path = 'temp'
filePath = os.path.join(path, 'graph.dot')
snap.SaveGViz(graph, filePath)
return os.path.abspath(filePath)
if net.GetIntAttrDatN(nid, "state") == 0:
NIdColorH[nid] = "green"
elif net.GetIntAttrDatN(nid, "state") == 1:
NIdColorH[nid] = "red"
else:
print "Error! There is infectious node in the network!"
#plotting the population over time
plt.plot(echo, susceptible, color='g')
plt.plot(echo, infectious, color='r')
plt.xlabel('Echos')
plt.ylabel('Nodes')
plt.title('SIS simulation over %d echos' % MAX_ECHO)
plt.savefig("SIS_%dtl_%dpb_%decho.png" %
(tl, contagion_probability_base, MAX_ECHO))
# Save the net in .dot file (plot with Gephi), snap.DrawGViz() too slow on big graph
snap.SaveGViz(net, "SISnet.dot", "SIR simulation network", True, NIdColorH)
#generate subgraph
V = snap.TIntV()
sub_node = []
for i in initial_list:
for connectedNode in net.GetNI(i).GetOutEdges():
sub_node = list(set(sub_node) | set([connectedNode]))
sub_node = list(set(sub_node) | set([i]))
for nid in sub_node:
V.Add(nid)
sub = snap.ConvertSubGraph(snap.PNEANet, net, V)
#Save subgraph
snap.SaveGViz(sub, "SISsub.dot", "SIR simulation subnet", True, NIdColorH)
#properties of largest strongly connected component
big_scc = snap.GetMxScc(repliesgraph)
snap.PrintInfo(big_scc, "Largest strongly connected component")
num_dir_edges = snap.CntUniqDirEdges(big_scc)
print "{0:.2f}% of directed edges are reciprocal".format(
snap.CntUniqBiDirEdges(big_scc) * 2 * 100 / num_dir_edges)
print "The clustering coefficient is {0:.2f}%".format(
snap.GetClustCf(big_scc) * 100)
print "The diameter is approximately {0}".format(
snap.GetBfsFullDiam(big_scc, 1000))
#store CC for graphviz
snap.SaveGViz(big_scc, filename + ".dot", "Largest Connected Component")
print "Saved GraphViz"
#page rank over entire graph to detect "celebrities"
PRankH = snap.TIntFltH()
snap.GetPageRank(repliesgraph, PRankH)
page_rank = {}
for userid in PRankH:
if PRankH[userid] > 0:
page_rank[userid] = PRankH[userid]
page_rank = sorted(page_rank.items(), key=lambda x: x[1], reverse=True)
print "Nodes with the highest page rank:"
celebs = map(lambda x: str(x[0]), page_rank[:100])
infos = twitterapi.lookup_user(user_id=','.join(celebs))
for info in infos:
print info['name'], info['verified']
sys.path.append("/courses/tsks11/ht2019/snap-4.1.0-4.1-centos6.5-x64-py2.6/")
TPATH = "/courses/tsks11/ht2019/data_and_fcns/session1/"
# PNGraph, a directed graph;
# PUNGraph, an undirected graph;
# PNEANet, a directed network;
# Generate a complete graph with 5 nodes (K5) and look at it via Graphviz
G = snap.GenFull(GraphType=snap.PUNGraph, Nodes=5)
# for EI in G.Edges():
# print "link from %d to %d" % (EI.GetSrcNId(), EI.GetDstNId())
# Saves Graph to the .DOT file format used by GraphViz
snap.SaveGViz(Graph=G,
OutFNm="_undirected-completely-connected.dot",
Desc="Undirected Completely Connected Network",
NodeLabels=True)
# Draws undirected graphs using the generated .dot file
os.system(
"neato -Tpdf _undirected-completely-connected.dot >_undirected-completely-connected.pdf"
)
# Generate a star graph and visualize in Graphviz
G = snap.GenStar(GraphType=snap.PNGraph, Nodes=10, IsDir=True)
# for EI in G.Edges():
# print "edge: (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId())
# Saves Graph to the .DOT file format used by GraphViz
snap.SaveGViz(Graph=G,
OutFNm="_directed-star.dot",
Desc="Directed Star Graph",
degree = node.GetDeg()
for i in range(degree):
if not node.GetNbrNId(i) in NIdV and not node.GetNbrNId(
i) in NNIdV and node.GetNbrNId(i) != nodefirst:
NNIdV.Add(node.GetNbrNId(i))
Nlist.Add(node.GetNbrNId(i))
Nlist.Add(nodefirst)
SubGraph = snap.GetSubGraph(Graph, Nlist)
labels = snap.TIntStrH()
for NI in SubGraph.Nodes():
labels[NI.GetId()] = word_of_dict[NI.GetId()]
snap.SaveGViz(SubGraph,
'..\\data\\subgraph\\subgraph{}.dot'.format(year),
"三农中的科技词图{}".format(year), labels)
alter(
"..\\data\\subgraph\\subgraph{}.dot".format(year),
'shape=ellipse, width=0.3, height=0.3',
'shape=ellipse, width=0.3, height=0.3, fontname="SimSun" size=\"7,7\"'
)
for i in NIdV:
alter2("..\\data\\subgraph\\subgraph{}.dot".format(year), i,
"#65934A")
for i in NNIdV:
alter2("..\\data\\subgraph\\subgraph{}.dot".format(year), i,
"#A0BF7C")
alter2("..\\data\\subgraph\\subgraph{}.dot".format(year), v, "#407434")