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()