def __init__(self, fileName1, fileName2, overlap_filename, examples,
directed):
self.rootDir = self.getPairsDir(fileName1) + "pairs"
self.fileName1 = fileName1
self.fileName2 = fileName2
#print os.path.abspath(overlap_filename)
self.df_overlap = pd.read_csv(os.path.abspath(overlap_filename),
index_col=None,
header=None)
self.df_overlap.columns = ['node_id', 'degree']
self.examples = examples
self.directed = directed
self.G1 = self.getGraph(fileName1)
print "[Load] G1."
self.G2 = self.getGraph(fileName2)
print "[Load] G2."
self.ndd1HG1 = snap.TIntStrH()
self.ndd2HG1 = snap.TIntStrH()
self.ndd1HG2 = snap.TIntStrH()
self.ndd2HG2 = snap.TIntStrH()
self.graph1Name = self.getGraphName(fileName1)
self.graph2Name = self.getGraphName(fileName2)
def snap_load(filename="", src_path="", directed=False):
graph_extension = ".graph"
label_extension = ".label"
if filename == "":
graph_extension = ""
if os.path.isfile(src_path + filename + graph_extension):
g_path = src_path + filename + graph_extension
FIn = snap.TFIn(g_path)
if directed:
G = snap.TNGraph.Load(FIn)
else:
G = snap.TUNGraph.Load(FIn)
elif os.path.isfile(src_path + filename + ".gml") or os.path.isfile(src_path + filename + ".gpickle"):
graph_converter.gml_gpickle_to_snap_graph(filename, src_path)
FIn = snap.TFIn(src_path + filename + graph_extension)
if directed:
G = snap.TNGraph.Load(FIn)
else:
G = snap.TUNGraph.Load(FIn)
if os.path.isfile(src_path + filename + label_extension):
FIn = snap.TFIn(src_path + filename + label_extension)
labels = snap.TIntStrH()
labels.Load(FIn)
else:
labels = snap.TIntStrH()
if labels:
return G, labels
else:
return G
def colorifyNetworkSnap(pajekInput, pajekOutput, myDatabase=''):
"""
This functions takes in a pajek file and output a pajek file. It assign a color label for each node
"""
#initialize an graph
G = snap.LoadPajek_PUNGraph(pajekInput)
#annotating netowrk nodes with raga labels and edges with distances
cmd1 = "select raagaId from file where id = (select file_id from pattern where id =%d)"
NIdColorH = snap.TIntStrH()
NIdLabelH = snap.TIntStrH()
try:
con = psy.connect(database=myDatabase, user=myUser)
cur = con.cursor()
print "Successfully connected to the server"
for NI in G.Nodes():
nid = NI.GetId()
cur.execute(cmd1 % (nid))
ragaId = cur.fetchone()[0]
print ragaId
NIdLabelH[nid] = str(ragaId)
NIdColorH[nid] = colors[str(ragaId)]
except psy.DatabaseError, e:
print 'Error %s' % e
if con:
con.rollback()
con.close()
sys.exit(1)
def __init__(self, graph, layout, printLabel=True):
# Degree of Edge: Role, Color, Weight, iterator
self.groups = {
0: [
['ClaimID', 'yellow', 0.4, 0],
],
1: [
['Doctor', 'red', 0.3, 0],
['Body Shop', 'yellow', 0.3, 0],
['Towing company', 'orange', 0.3, 0],
['Inspector', 'blue', 0.3, 0],
['Lawyer', 'purple', 0.2, 0],
],
2: [
['Claimant', 'green', 0.7, 0],
]
}
self.G = graph
self.layout = layout
self.printLabel = printLabel
self.labels = snap.TIntStrH()
self.NIdColorH = snap.TIntStrH()
self.nameList = {} # nameList is only been assigned once
def q2_3():
print("============")
g = load_graph_weights()
v_mat = cal_initial_feature(g)
for k in range(2):
v_mat = aggregrate(v_mat, g)
v_9 = v_mat[9]
cos_sim = [(i, cal_cos_sim(v_9, v)) for i, v in enumerate(v_mat)]
cos_sim.sort(key=lambda y: y[1], reverse=True)
cos_sims = [u[1] for u in cos_sim]
plt.hist(cos_sims, bins=20)
plt.title("distribution of cosine similarity")
plt.show()
node_1, sim_1 = find_node(0, 0.05, cos_sim)
subg_1 = get_2_nd_subgraph(node_1, g)
subg_1_h = snap.TIntStrH()
subg_1_h[node_1] = "blue"
subg_1 = snap.ConvertSubGraph(snap.PUNGraph, g, subg_1)
snap.DrawGViz(subg_1, snap.gvlNeato, "subgraph_1.png",
"subgraph 1 sim: {0}".format(round(sim_1,
2)), True, subg_1_h)
node_2 = 9 #find_node(0.4, 0.45, cos_sim)
subg_2 = get_2_nd_subgraph(node_2, g)
subg_2_h = snap.TIntStrH()
subg_2_h[node_2] = "blue"
subg_2 = snap.ConvertSubGraph(snap.PUNGraph, g, subg_2)
snap.DrawGViz(subg_2, snap.gvlNeato, "subgraph_center_9.png",
"subgraph_center_9", True, subg_2_h)
node_3, sim_3 = find_node(0.6, 0.65, cos_sim)
subg_3 = get_2_nd_subgraph(node_3, g)
subg_3_h = snap.TIntStrH()
subg_3_h[node_3] = "blue"
subg_3 = snap.ConvertSubGraph(snap.PUNGraph, g, subg_3)
snap.DrawGViz(subg_3, snap.gvlNeato, "subgraph_3.png",
"subgraph 3 sim: {0}".format(round(sim_3,
2)), True, subg_3_h)
node_4, sim_4 = find_node(0.9, 0.95, cos_sim)
subg_4 = get_2_nd_subgraph(node_4, g)
subg_4_h = snap.TIntStrH()
subg_4_h[node_4] = "blue"
subg_4 = snap.ConvertSubGraph(snap.PUNGraph, g, subg_4)
snap.DrawGViz(subg_4, snap.gvlNeato, "subgraph_4.png",
"subgraph 4 sim: {0}".format(round(sim_4,
2)), True, subg_4_h)
print("============")
def genNDDH(self, G):
ndd1H = snap.TIntStrH()
ndd2H = snap.TIntStrH()
n = G.GetNodes()
print "# of nodes: %d" % n
i = 0
for node in G.Nodes():
nddV = self.get1HNDD(G, node)
ndd1H[node.GetId()] = self.asString(nddV)
nddV = self.get2HNDD(G, node)
ndd2H[node.GetId()] = self.asString(nddV)
i += 1
progress = float(i) / n
print "[progress] %f\n" % progress
return ndd1H, ndd2H
def createVectLabel(self, attrbToLabel, vertice, xmlFileName):
root = self.startRootNetwork(xmlFileName)
temp = vertice.split('-')
dictLabel = snap.TIntStrH()
for metaNetwork in root.getchildren():
for nodesType in metaNetwork.getchildren():
for nodeclass in nodesType.getchildren():
nodeclassType = nodeclass.get("type")
if (nodeclassType != temp[0]):
continue
for node in nodeclass.getchildren():
verticeName = node.get("id")
if attrbToLabel == "mainId":
dictLabel[int(verticeName)] = verticeName
continue
for nodeProperties in node.getchildren():
for property in nodeProperties.getchildren():
# To include only attributes that the were selected in the var self.attribSelecteds
name = property.get('name')
value = property.get('value')
if name == attrbToLabel:
dictLabel[int(verticeName)] = value
return dictLabel
def __init__(self, filename, mVals, pVal, tau):
self.fileName = fileName
self.mVals = mVals
self.pVal = pVal
self.pVals = []
self.pVals.append(pVal)
self.pVals.append(1 - pVal)
self.tau = tau
self.nLH = snap.TIntStrH()
self.lblNH = snap.TStrIntH() # Node count with attached label
self.lblEH = snap.TIntIntH() # Edge count with attached src dst labels
self.RH = snap.TIntFltPrH()
self.BH = snap.TIntFltPrH()
self.cRV = snap.TIntV()
self.cBV = snap.TIntV()
self.G = self.getGraph(snap.PUNGraph)
self.NG = snap.TNEANet()
self.graphName = self.getGraphName()
self.rootDir = self.getParentDir(self.fileName)
self.absrootDir = os.path.abspath(self.rootDir)
self.cR_count = 0
self.cB_count = 0
self.RH_count = 0
self.BH_count = 0
def create_rnd_trees(size, number, filename, dst_path, labeled=False, seed=1):
random.seed(seed)
for i in range(number):
G = nx.random_tree(size, seed + i)
GSnap = snap.TUNGraph()
if labeled:
labels = snap.TIntStrH()
for node in G.nodes(data=True):
id = node[0]
if labeled:
node_label = node[1]['predicate']
labels[int(id)] = str(node_label)
GSnap.AddNode(int(id))
for edge in G.edges():
GSnap.AddEdge(int(edge[0]), int(edge[1]))
FOut = snap.TFOut(dst_path + filename + "_" +
str(i).zfill(math.ceil(math.log10(number + 1))) +
".graph")
GSnap.Save(FOut)
FOut.Flush()
if labeled:
FOut = snap.TFOut(dst_path + filename + "_" +
str(i).zfill(math.ceil(math.log10(number + 1))) +
".labels")
labels.Save(FOut)
FOut.Flush()
def gml_gpickle_to_snap_graph(filename, src_path, dst_path=""):
if dst_path == "":
dst_path = src_path
try:
G = nx.read_gpickle(src_path + filename + ".gpickle")
except:
G = nx.read_gml(src_path + filename + ".gml")
GSnap = snap.TUNGraph()
labels = snap.TIntStrH()
for node in G.nodes(data=True):
id = node[0]
node_label = node[1]['predicate']
labels[int(id)] = str(node_label)
GSnap.AddNode(int(id))
for edge in G.edges():
GSnap.AddEdge(int(edge[0]), int(edge[1]))
FOut = snap.TFOut(dst_path + filename + ".graph")
GSnap.Save(FOut)
FOut = snap.TFOut(dst_path + filename + ".labels")
labels.Save(FOut)
FOut.Flush()
def drawGraph(self,labelDict,theGraph,graphName):
labels = snap.TIntStrH()
for NI in theGraph.Nodes():
thekey=self.nid2id[NI.GetId()]
labels[NI.GetId()] = str(labelDict[thekey])
snap.DrawGViz(theGraph, snap.gvlSfdp, graphName, " ", labels)
def build_legend(filename):
legend = snap.TIntStrH()
with open(filename) as f:
for line in f:
tokens = line.split()
if not tokens[0].isdigit(): continue # Ignore the first line
key = int(tokens[0])
legend[key] = ' '.join(tokens[1:])
return legend
def main():
graphs, names = load_networks(IN_FOLDER)
for g, n in zip(graphs, names):
NIdColorH = snap.TIntStrH()
if g.GetNodes() < 1000:
for i in g.Nodes():
NIdColorH[i.GetId()] = "red"
snap.DrawGViz(g, snap.gvlCirco, OUT_FOLDER + n + ".png", n, False,
NIdColorH)
def DrawGraph(lower, upper, cos_sim, g, filename, title='', color='blue'):
"""
output the graph
"""
sub = GetSubgraph(FindNode(lower, upper, cos_sim), g)
whole_graph = snap.TIntStrH()
whole_graph[sub[0]] = color
sub = snap.ConvertSubGraph(snap.PUNGraph, g, sub)
snap.DrawGViz(sub, snap.gvlNeato, filename, title, True, whole_graph)
def plot_subgraph(graph, nodes, output, title):
node_ids_vector = snap.TIntV()
labels = snap.TIntStrH()
for pair in nodes:
if pair[0] not in node_ids_vector:
node_ids_vector.Add(pair[0])
pkg = graph.GetStrAttrDatN(pair[0], "pkg")
labels[pair[0]] = ellipsize_text(pkg, 30)
subgraph = snap.GetSubGraph(graph, node_ids_vector)
snap.DrawGViz(subgraph, snap.gvlNeato, output, title, labels)
def save_graph(self):
labels = snap.TIntStrH()
filled_nodes = self.assignment.keys()
# assert(len(filled_nodes) > 0)
for i in range(self.graph.GetNodes()):
if i in filled_nodes:
labels[i] = str(i) + ": " + self.assignment[i]
else:
labels[i] = str(i) + ": "
snap.DrawGViz(self.graph, snap.gvlDot, "assignment.png", " ", labels)
def GraphVisualization(G1):
labels = snap.TIntStrH()
# page_name = snap.TIntStrH()
# page_type = snap.TIntStrH()
for NI in G1.Nodes():
labels[NI.GetId()] = str(NI.GetId())
# page_name[NI.GetId()] = str(targets['page_name'][NI.GetId()])
# page_type[NI.GetId()] = str(targets['page_type'][NI.GetId()])
snap.DrawGViz(G1, snap.gvlDot, "output.png", " ", labels)
def build_legends(filename, valuefn, delim=None):
legend = snap.TIntStrH()
full_lines = { }
with open(filename) as f:
for line in f:
tokens = line.split(delim)
if not tokens[0].isdigit(): continue # Ignore the first line
key = int(tokens[0])
legend[key] = valuefn(tokens)
full_lines[key] = tokens[1:]
return (legend, full_lines)
def buildNetwork(devices):
vector = {}
home_network = snap.TUNGraph.New()
labels = snap.TIntStrH()
dev_ind = 0
for device in devices:
dev_ind+=1
home_network.AddNode(dev_ind)
if dev_ind!=1:
home_network.AddEdge(dev_ind, 1)
labels.AddDat(dev_ind, str(device))
vector = buildVector(device,vector)
return home_network, labels, vector
def betweenCentral(self,date):
temp = self.network
temp.AddFltAttrN("bcentrality")
# Colour Hash Table
NIdColorH = snap.TIntStrH()
purple = 0
blue = 0
green = 0
yellow = 0
red = 0
# For every node
for NI in temp.Nodes():
# Get ITs betweenes centrality
CloseCentr = snap.GetClosenessCentr(temp, NI.GetId())
temp.AddFltAttrDatN(NI.GetId(),CloseCentr,"bcentrality")
# Determine colour
if CloseCentr <0.2:
NIdColorH[NI.GetId()] = "purple"
purple+=1
elif CloseCentr <0.4:
NIdColorH[NI.GetId()] = "blue"
blue+=1
elif CloseCentr <0.6:
NIdColorH[NI.GetId()] = "green"
green+=1
elif CloseCentr <0.8:
NIdColorH[NI.GetId()] = "yellow"
yellow+=1
else:
NIdColorH[NI.GetId()] = "red"
red+=1
print"Purple:\t", purple
print"Blue:\t" , blue
print"Green:\t", green
print"Yellow:", yellow
print"Red:\t" ,red
# Draw graph
#snap.DrawGViz(temp, snap.gvlSfdp, "BetweenesCentrality.png", date, True, NIdColorH)
"""
def main():
cosine_dists = get_cosine_dist()
Graph = snap.TUNGraph.New()
name_to_id = add_nodes(Graph)
for pair in cosine_dists:
if cosine_dists[pair] > 0.5:
Graph.AddEdge(name_to_id[pair[0]], name_to_id[pair[1]])
labels = snap.TIntStrH()
for name in name_to_id:
labels[name_to_id[name]] = name
snap.DrawGViz(Graph, snap.gvlDot, OUTPUT_GRAPH, " ", labels)
def PlotSubGraph(G,NodeID,path="./graph/"):
Node = G.GetNI(NodeID)
NIdV = snap.TIntV() #subgraph nodes ID
NIdV.Add(NodeID)
Deg = Node.GetDeg()
NidName = snap.TIntStrH()
NidName[NodeID] = str(NodeID)
for i in range(Deg):
NbrID = Node.GetNbrNId(i)
NIdV.Add(NbrID)
NidName[NbrID] = str(NbrID)
SubGraph = snap.GetSubGraph(G, NIdV)
snap.DrawGViz(SubGraph,snap.gvlDot,path+"subgraph"+str(NodeID)+".png","SubGraph of "+str(NodeID),NidName)
return 0
def visualize_graph(file_egdes, file_img, list_comunity):
graph = snap.LoadEdgeList(snap.PUNGraph, file_egdes, 0, 1, '\t')
NIdColorH = snap.TIntStrH()
colors = ["red", "yellow", "brown", "blue", "green", "pink", "indigo", "antiquewhite","chocolate", "purple", "sienna4", "powderblue", "violet" ]
num_color = len(colors)
for i, comunity in enumerate(list_comunity):
if len(comunity) ==1:
NIdColorH[comunity[0]]= "white"
else:
index = i%num_color
for node in comunity:
NIdColorH[node] = colors[index]
snap.DrawGViz(graph, snap.gvlNeato, file_img, "graph top 200 nodes", True, NIdColorH)
def getNodeIdLabel(self, G):
gLH = snap.TIntStrH()
for EI in G.Edges():
srcId = EI.GetSrcNId()
dstId = EI.GetDstNId()
EI = G.GetEI(srcId, dstId)
srcAttr = "srcLabel"
srcAttrVal = G.GetStrAttrDatE(EI, srcAttr)
dstAttr = "dstLabel"
dstAttrVal = G.GetStrAttrDatE(EI, dstAttr)
gLH[srcId] = srcAttrVal
gLH[dstId] = dstAttrVal
return gLH
def create_graph_from_likes(filename):
myfile = open(filename, "r")
all_data = json.load(myfile)
# loading part of the graph
count = 0
data = {}
for post, likes in all_data.items():
if count == 100: break
count += 1
data[post] = likes
graph = snap.TUNGraph.New()
names = snap.TIntStrH()
posts_ints = {}
users_ints = {}
p = 0
k = 3000
for post, likes in data.items():
posts_ints[post] = p
graph.AddNode(p)
for like in likes:
# user_int_id = -1
if like['id'] in users_ints.keys():
user_int_id = users_ints[like['id']]
names[user_int_id] = like['name'].encode("utf8")
else:
users_ints[like['id']] = k
user_int_id = k
graph.AddNode(user_int_id)
k += 1
graph.AddEdge(p, user_int_id)
p += 1
print "G1: Nodes %d, Edges %d" % (graph.GetNodes(), graph.GetEdges())
return graph
for item in b: if item not in proxy: proxy[item]=count rev_proxy[count]=item count=count+1 if b[item] not in proxy: proxy[b[item]]=count rev_proxy[count]=b[item] count=count+1 for item in proxy: n1.AddNode(proxy[item]) for item in b: if b[item] != 0: n1.AddEdge(proxy[b[item]],proxy[item]) NIdColorH = snap.TIntStrH() OutDegV = snap.TIntPrV() snap.GetNodeOutDegV(n1, OutDegV) for item in OutDegV: if item.GetVal2()>=3: NIdColorH[item.GetVal1()]="green" print rev_proxy[item.GetVal1()], item.GetVal2() if item.GetVal2()==0: NIdColorH[item.GetVal1()]="black" snap.DrawGViz(n1, snap.gvlDot, "graph3.png", "graph 1",False,NIdColorH) snap.DrawGViz(n1, snap.gvlNeato, "graph1.png", "graph 1",False,NIdColorH) snap.DrawGViz(n1, snap.gvlCirco, "graph2.png", "graph 2",False,NIdColorH)
print("Iter < Hash.EndI", Iter < Hash.EndI())
#while Iter < Hash.EndI():
while not Iter.IsEnd():
Key = Iter.GetKey()
Value = Iter.GetDat()
print(Key, Value)
Iter.Next()
for item in Hash:
print(item, Hash[item])
print("---------- 2 ---------")
h = snap.TIntStrH()
h[5] = "five"
h[3] = "thre"
h[9] = "nine"
h[6] = "six"
h[1] = "one"
print(h.Len())
print("h[3] =", h[3])
h[3] = "three"
print("h[3] =", h[3])
def runSIR(G, init_adopter, p, tl, initial_list=None):
number_node = G.GetNodes()
number_edge = G.GetEdges()
if initial_list is None:
initial_list = random.sample(range(number_node), init_adopter)
# data to return
infectious_num = []
removed_num = []
final_states = []
# 0: Susceptible, 1:Infectious, 2: Removed
labels = snap.TIntStrH()
for NI in G.Nodes():
nid = NI.GetId()
state = 0
if nid in initial_list:
G.AddIntAttrDatN(nid, 1, "state")
state = 1
else:
G.AddIntAttrDatN(nid, 0, "state")
state = 0
G.AddIntAttrDatN(nid, 0, "step")
number_R = number_node - len(initial_list)
number_echo = 0
while number_R != number_node:
for NI in G.Nodes():
nid = NI.GetId()
ival = G.GetIntAttrDatN(nid, "state")
# if it's infectious, it will infect its neighbors.
if ival == 1:
for nid1 in NI.GetOutEdges():
att_value = G.GetIntAttrDatN(nid1, "state")
# with probability of p=0.2, a susceptible node will be infected.
if (np.random.choice(np.arange(0, 2), p=[p, 1 - p])
== 0) and (att_value == 0):
# The state 3 is temporary in order to avoid repeated infection.
G.AddIntAttrDatN(nid1, 3, "state")
# update the step of infection.
step_this = G.GetIntAttrDatN(nid, "step")
step_this += 1
G.AddIntAttrDatN(nid, step_this, "step")
if step_this == tl:
G.AddIntAttrDatN(nid, 2, "state")
# Count the number of susceptible or removed nodes.
number_R_temp = 0
removed = 0
for NI in G.Nodes():
nid = NI.GetId()
ival = G.GetIntAttrDatN(nid, "state")
if ival == 3: # The nodes should be updated as infectious now.
G.AddIntAttrDatN(nid, 1, "state")
ival = 1
if ival == 0 or ival == 2:
number_R_temp += 1
if ival == 2:
removed += 1
number_R = number_R_temp
number_echo += 1
infectious_num.append(number_node - number_R)
removed_num.append(removed)
#print(number_R)
for NI in G.Nodes():
nid = NI.GetId()
ival = G.GetIntAttrDatN(nid, "state")
step_number = G.GetIntAttrDatN(nid, "step")
final_states.append((nid, ival, step_number))
# print(nid, ival, step_number)
print("One round of simulation completes.")
return infectious_num, removed_num, final_states
edge = [] #initializes edge
for jsonindex in range(len(jsons)):
DstNId = userdict[destnodes[jsonindex]] #integer source node id
file = jsons[jsonindex]
weights = jsons[jsonindex].values()
rowindex = 0
for key in file:
short = re.search('user=(.+?)&hl', key).group(1)
SrcNId = userdict[short] #this should be the contents of the json
edge.append([SrcNId, DstNId, weights[rowindex], sizeindex
]) #source node, destination node, edge weight, edge id
rowindex = rowindex + 1 #index for each keys in json
sizeindex = sizeindex + 1 #row index of edge matrix
labels = snap.TIntStrH()
for index in range(sizeindex):
#labels[index]=nodedict[edge[index][0]]
labels.AddDat(index, nodedict[edge[index][0]])
#Creates network object
G1 = snap.TNEANet.New()
#Adds Nodes
for node in nodedict:
G1.AddNode(node)
#Adds Weight Attribute
G1.AddIntAttrE('Weight')
#Adds Edges
def hops_estimation_recursive_snap(G,
H,
G_labels=snap.TIntStrH(),
H_labels=snap.TIntStrH(),
k=0,
runtime=0,
labeled=True,
detailed_estimation=False):
"""
:param G: graph
:param H: pattern graph
:param G_labels: vector of graph labels
:param H_labels: vector of pattern labels
:param k: number of iterations of hops for one iteration
:param runtime: runtime of hops for one estimation
:param labeled: determines if graph and pattern are labeled
:param detailed_estimation: if output should be detailed
:return: in case of detailed estimation return estimation count and single iteration steps, else only return estimation count
"""
estimation_values = []
if labeled:
pattern_root_node, root_node_predicate_name = get_pattern_root_node_snap(
H_labels, label_frequency_histogram_snap(G_labels))
possible_root_nodes = [
x.GetId() for x in G.Nodes()
if G_labels[x.GetId()] == root_node_predicate_name
]
else:
pattern_root_node = H.GetRndNId()
possible_root_nodes = [x.GetId() for x in G.Nodes()]
if runtime != 0:
t_end = time.time() + runtime
while time.time() < t_end:
v = possible_root_nodes[random.randint(
0,
len(possible_root_nodes) - 1)]
c, phi, phi_inv = find_tree_embeddings_snap(
pattern_root_node, v, {pattern_root_node: v},
{v: pattern_root_node}, H, H_labels, G, G_labels, labeled)
c *= len(possible_root_nodes)
estimation_values.append(c)
else:
for i in range(0, k):
# sample root node and sample first image of u
v = possible_root_nodes[random.randint(
0,
len(possible_root_nodes) - 1)]
c, phi, phi_inv = find_tree_embeddings_snap(
pattern_root_node, v, {pattern_root_node: v},
{v: pattern_root_node}, H, H_labels, G, G_labels, labeled)
c *= len(possible_root_nodes)
estimation_values.append(c)
if len(estimation_values) == 0:
return 0, [0]
if detailed_estimation:
return sum(estimation_values) / len(
estimation_values), estimation_values
else:
return sum(estimation_values) / len(estimation_values)
