def splitGraph(graph, random=True, p=1 / float(3)):
all_edges = graph.getAllEdges()
vertices = graph.vlist
inc = bernoulli.rvs(p, size=len(all_edges))
train = SparseGraph(vertices)
test = SparseGraph(vertices)
train.addEdges(all_edges[inc == 0])
test.addEdges(all_edges[inc == 1])
return train, test
def SmallWorld(n):
# slow
graph = SparseGraph(n)
generator = SmallWorldGenerator(0.3, 50)
graph = generator.generate(graph)
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
def BarabasiAlbertEdgeList(n):
graph = SparseGraph(n)
generator = BarabasiAlbertGenerator(10, 10)
graph = generator.generate(graph)
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
def toSparseGraph(self):
"""
Convert the current graph to a SparseGraph. Currently, vertex labels
are not converted.
"""
from apgl.graph import SparseGraph
W = self.getSparseWeightMatrix(format="csr")
graph = SparseGraph(W.shape[0], W=W, undirected=self.undirected)
return graph
def ConfigurationModel(edges_list):
deg_dict = defaultdict(int)
for u, v in edges_list:
deg_dict[v] += 1
l = array(deg_dict.values())
n = len(l)
graph = SparseGraph(n)
generator = ConfigModelGenerator(l)
graph = generator.generate(graph)
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
def KroneckerEdgeList(n):
init = SparseGraph(4)
init[0, 1] = 1
init[0, 2] = 1
init[0, 3] = 1
for i in range(4):
init[i, i] = 1
k = int(log(n, 4)) + 1
generator = KroneckerGenerator(init, k)
graph = generator.generate()
l, _ = graph.adjacencyList()
return convertAdjListToEdgeList(l)
def adj_from_listfile(adj_list, n, vertices=False, skip=4):
if not vertices:
vertices = range(n)
adj = SparseGraph(len(vertices))
print "sparse graph created"
adj1, adj2 = getList(adj_list, skip)
for i, j in zip(adj1, adj2):
if i >= n:
break
if j >= n:
pass
elif i in vertices and j in vertices:
adj[i, j] = 1
print "adj list constructed"
return adj
def adj_from_listfile(adj_list, n, vertices=False, skip=4):
if not vertices:
vertices = range(n)
adj = SparseGraph(len(vertices))
with open(adj_list, 'r') as a:
k = 0
for line in a:
if k < skip:
k += 1
else:
i, j = line.split()
i = int(i)
j = int(j)
if i in vertices and j in vertices:
adj[i, j] = 1
adj[j, i] = 1
return adj
def __next__(self):
if self.emit == False:
# We need a "do while" loop here to manage situations where self.graphIterator is already exhausted
while True:
W = next(self.graphIterator)
graph = SparseGraph(W.shape[0], W=W)
numComponents = len(graph.findConnectedComponents())
if __debug__:
logging.debug("graph size = " + str(graph.size) +
" num components = " + str(numComponents))
if numComponents < self.maxComponents:
# self.emit = True
break
else:
W = self.graphIterator.next()
return W
def ingest(filename):
f = open(filename, "r")
# exit if file not in readmode
if f.mode != 'r':
exit()
# stream graph text file
content = f.readlines()
f.close()
# retrieve meta data
cadMeta = content[0].split(" ")
# get number of nodes
numNodes = int(cadMeta[0])
# get number of time sequences
t = int(cadMeta[1])
# generate t amount of graphs
G = []
for _ in range(t):
G.append(SparseGraph(numNodes))
# iterate through every node connection
# store in sparse graph for specific time sequence
for line in content[1:]:
data = line.split(" ")
n1 = int(data[0])
n2 = int(data[1])
w = int(data[2])
t = int(data[3])
G[t][n1, n2] = w
return G
def dijkstra(graph):
#pre process graph
adj = graph.adjacencyList()
connections = adj[0]
weights = adj[1]
nvert = graph.getNumVertices()
nodes = list(range(nvert))
distances = {}
for i in range(nvert): # i = source
distances.update({i: dict(zip(connections[i], weights[i]))})
ct = SparseGraph(nvert)
for i in range(nvert):
unvisited = {node: None for node in nodes} #using None as +inf
visited = {}
current = i
currentDistance = 0
unvisited[current] = currentDistance
while True:
for neighbour, distance in distances[current].items():
if neighbour not in unvisited: continue
newDistance = currentDistance + distance
if unvisited[neighbour] is None or unvisited[
neighbour] > newDistance:
unvisited[neighbour] = newDistance
visited[current] = currentDistance
if i != current:
ct[i, current] = currentDistance
del unvisited[current]
if not unvisited: break
candidates = [node for node in unvisited.items() if node[1]]
current, currentDistance = sorted(candidates,
key=lambda x: x[1])[0]
return ct
def match(self, graph1, graph2):
"""
Take two graphs are match them. The two graphs must be AbstractMatrixGraphs
with VertexLists representing the vertices.
:param graph1: A graph object
:param graph2: The second graph object to match
:return permutation: A vector of indices representing the matching of elements of graph1 to graph2
:return distance: The graph distance list [graphDistance, fDistance, fDistanceExact]
"""
#Deal with case where at least one graph is emty
if graph1.size == 0 and graph2.size == 0:
permutation = numpy.array([], numpy.int)
distanceVector = [0, 0, 0]
time = 0
return permutation, distanceVector, time
elif graph1.size == 0 or graph2.size == 0:
if graph1.size == 0:
graph1 = SparseGraph(
VertexList(graph2.size,
graph2.getVertexList().getNumFeatures()))
else:
graph2 = SparseGraph(
VertexList(graph1.size,
graph1.getVertexList().getNumFeatures()))
numTempFiles = 5
tempFileNameList = []
for i in range(numTempFiles):
fileObj = tempfile.NamedTemporaryFile(delete=False)
tempFileNameList.append(fileObj.name)
fileObj.close()
configFileName = tempFileNameList[0]
graph1FileName = tempFileNameList[1]
graph2FileName = tempFileNameList[2]
similaritiesFileName = tempFileNameList[3]
outputFileName = tempFileNameList[4]
if self.useWeightM:
W1 = graph1.getWeightMatrix()
W2 = graph2.getWeightMatrix()
else:
W1 = graph1.adjacencyMatrix()
W2 = graph2.adjacencyMatrix()
numpy.savetxt(graph1FileName, W1, fmt='%.5f')
numpy.savetxt(graph2FileName, W2, fmt='%.5f')
#Compute matrix similarities
C = self.vertexSimilarities(graph1, graph2)
numpy.savetxt(similaritiesFileName, C, fmt='%.5f')
#Write config file
configFile = open(configFileName, 'w')
configStr = "graph_1=" + graph1FileName + " s\n"
configStr += "graph_2=" + graph2FileName + " s\n"
configStr += "C_matrix=" + similaritiesFileName + " s\n"
configStr += "algo=" + self.algorithm + " s\n"
configStr += "algo_init_sol=" + self.init + " s\n"
configStr += "alpha_ldh=" + str(self.alpha) + " d\n"
configStr += "cdesc_matrix=A c\n"
configStr += "cscore_matrix=A c\n"
configStr += "hungarian_max=10000 d\n"
configStr += "algo_fw_xeps=0.01 d\n"
configStr += "algo_fw_feps=0.01 d\n"
configStr += "dummy_nodes=0 i\n"
configStr += "dummy_nodes_fill=" + str(self.rho) + " d\n"
configStr += "dummy_nodes_c_coef=" + str(self.gamma) + " d\n"
configStr += "qcvqcc_lambda_M=" + str(self.lambdaM) + " d\n"
configStr += "qcvqcc_lambda_min=1e-3 d\n"
configStr += "blast_match=0 i\n"
configStr += "blast_match_proj=0 i\n"
configStr += "exp_out_file=" + outputFileName + " s\n"
configStr += "exp_out_format=Compact Permutation s\n"
configStr += "verbose_mode=0 i\n"
configStr += "verbose_file=cout s\n"
configFile.write(configStr)
configFile.close()
fnull = open(os.devnull, 'w')
home = expanduser("~")
argList = [home + "/.local/bin/graphm", configFileName]
subprocess.call(argList, stdout=fnull, stderr=fnull)
fnull.close()
#Next: parse input files
outputFile = open(outputFileName, 'r')
line = outputFile.readline()
line = outputFile.readline()
line = outputFile.readline()
line = outputFile.readline()
graphDistance = float(outputFile.readline().split()[2])
fDistance = float(outputFile.readline().split()[2])
fDistanceExact = float(outputFile.readline().split()[2])
time = float(outputFile.readline().split()[1])
line = outputFile.readline()
line = outputFile.readline()
permutation = numpy.zeros(
max(graph1.getNumVertices(), graph2.getNumVertices()), numpy.int)
i = 0
for line in outputFile:
permutation[i] = int(line.strip()) - 1
i += 1
#Delete files
os.remove(graph1FileName)
os.remove(graph2FileName)
os.remove(similaritiesFileName)
os.remove(configFileName)
os.remove(outputFileName)
distanceVector = [graphDistance, fDistance, fDistanceExact]
return permutation, distanceVector, time
""" Name: Generate Graph: Author: Jia_qiu Wang(王佳秋) Data: December, 2016 function: """ from apgl.graph import GeneralVertexList, SparseGraph import numpy numVertices = 5 # 顶点个数 graph = SparseGraph(numVertices) # 具有5个顶点个数的图数据结构 graph[0, 1] = 1 graph[0, 2] = 3 graph[1, 2] = 0.1 graph[3, 4] = 2 graph.setVertex(0, "abc") graph.setVertex(1, 123) print(graph.findConnectedComponents()) # 输出联通分量 print(graph.getWeightMatrix()) # 输出图的邻接矩阵 # print(graph.degreeDistribution()) print(graph.neighbours(0)) print(graph)
r2 = max((k - r), 0)
sigmaSqSum = (sigma[0:r2]**2).sum()
bound = gammaSqSum + lmbdaSqSum - 2 * sigmaSqSum
print("r=" + str(r))
print("gammaSqSum=" + str(gammaSqSum))
print("lmbdaSqSum=" + str(lmbdaSqSum))
print("sigmaSqSum=" + str(sigmaSqSum))
return bound
#Change to work with real Laplancian
numRows = 100
graph = SparseGraph(GeneralVertexList(numRows))
p = 0.1
generator = ErdosRenyiGenerator(p)
graph = generator.generate(graph)
print(graph)
AA = graph.normalisedLaplacianSym()
p = 0.001
generator.setP(p)
graph = generator.generate(graph, requireEmpty=False)
AA2 = graph.normalisedLaplacianSym()
