Python networkx.average_clustering函数代码示例

def main():
    tempo_dir = "../corpus-local/tempo-txt"
    file_regex = ".*\.txt"

    G = build_graph(tempo_dir, file_regex)
    """
  ccs = nx.clustering(G)
  avg_clust = sum(ccs.values()) / len(ccs)
  """
    print tempo_dir
    print "\tAda " + str(len(G.nodes())) + " node."
    print "\tAda " + str(len(G.edges())) + " edge."
    print "\tClustering coefficient      : " + str(nx.average_clustering(G))
    print "\tAverage shortest path length"
    for g in nx.connected_component_subgraphs(G):
        print "\t\t" + str(nx.average_shortest_path_length(g))

    kompas_dir = "../corpus-local/kompas-txt"
    G = build_graph(kompas_dir, file_regex)
    print kompas_dir
    print "\tAda " + str(len(G.nodes())) + " node."
    print "\tAda " + str(len(G.edges())) + " edge."
    print "\tClustering coefficient      : " + str(nx.average_clustering(G))
    print "\tAverage shortest path length"
    for g in nx.connected_component_subgraphs(G):
        print "\t\t" + str(nx.average_shortest_path_length(g))

def check_and_merge_clusters(index):
    global clusters
    global G
        
    given_cluster = []
    total_clusters = len(clusters)
    cluster_coeff_all = [0]*total_clusters
    cluster_coeff_temp = [0]*total_clusters
    for string in clusters[index]:
        given_cluster.append(int(string))
    given_graph = G.subgraph(given_cluster)
    clustering_coeff_given   = nx.average_clustering(given_graph)
    
    temp_index = 0
    while temp_index < total_clusters:
        temp_cluster = []
        for string in clusters[temp_index]:
            temp_cluster.append(int(string))
        temp_graph = G.subgraph(temp_cluster)
        temp_graph_all = G.subgraph(temp_cluster + given_cluster)

        clustering_coeff_all = nx.average_clustering(temp_graph_all)
        clustering_coeff_temp = nx.average_clustering(temp_graph)
        cluster_coeff_all[temp_index] = clustering_coeff_all
        cluster_coeff_temp[temp_index] = clustering_coeff_temp        
        temp_index = temp_index + 1

    # Find the index with highest coefficient and combine them
    max_index = cluster_coeff_all.index(max(cluster_coeff_all))
    if clustering_coeff_given > .94:
        clustering_coeff_given = 0.94
    if cluster_coeff_temp[max_index] > .94:
        cluster_coeff_temp[max_index] =0.94
    if (cluster_coeff_all[max_index] >= .95*clustering_coeff_given) and (cluster_coeff_all[max_index] >= .95*cluster_coeff_temp[max_index]):
        combine_cluster(index, max_index)

def can_combine_cluster(cl1, cl2):
    global G
    cl1_int = []
    cl2_int = []
    for string in cl1:
        cl1_int.append(int(string))
    for string in cl2:
        cl2_int.append(int(string))

    temp_graph1 = G.subgraph(cl1_int)
    temp_graph2 = G.subgraph(cl2_int)
    temp_graph_all = G.subgraph(cl1_int + cl2_int)

    clustering_coeff_1 = nx.average_clustering(temp_graph1)
    clustering_coeff_2 = nx.average_clustering(temp_graph2)
    clustering_coeff_all = nx.average_clustering(temp_graph_all)
    # print (str)(clustering_coeff_1) + " " + (str)(clustering_coeff_2) +" "+ (str)(clustering_coeff_all)

    if clustering_coeff_1 == 1:
        clustering_coeff_1 = 0.96

    if clustering_coeff_2 == 1:
        clustering_coeff_2 = 0.96

    if (clustering_coeff_1 == 0) and (clustering_coeff_2 == 0):
        return False

    fraction = 0.95
    if (clustering_coeff_all > fraction * clustering_coeff_1) and (
        clustering_coeff_all > fraction * clustering_coeff_2
    ):
        # print "combine"
        return True
    return False

def MvsD(A, Au, M, D):
    """docstring for MvsD"""
    #Calculate the number of nodes
    print("Number of nodes in A  : " + str(len(A.nodes())))
    print("Number of nodes in Au : " + str(len(Au.nodes())))
    #Calculate the number of links
    print("Number of links in A  : " + str(len(A.edges())))
    print("Number of links in Au : " + str(len(Au.edges())))
    t = nx.average_clustering(Au)
    print("network clustering coefficient for Au : " + str(t))
    print("")
    #Calculate the number of nodes
    print("Number of nodes in M  : " + str(len(M.nodes())))
    print("Number of nodes in D : " + str(len(D.nodes())))
    t = nx.average_clustering(M)
    print("network clustering coefficient for M  : " + str(t))
    t = nx.average_clustering(D)
    print("network clustering coefficient for D : " + str(t))
    
    
    MavgD = float(sum(M.degree().values()))/float(len(M.nodes()))
    print("Connectivity M : " + str(MavgD))
    DavgD = float(sum(D.degree().values()))/float(len(D.nodes()))
    print("Connectivity D : " + str(DavgD))
    
    pass

def t_t_cc(path=r"d:\data\9.txt"):
    rstr = ''
    g = nx.Graph()
    g = read_file_txt(g, path)
    w = [14,13,12,6]
    print nx.average_clustering(g)
    for each in w:
        R=gRa(g,each)
        pg=r_perturbR(g, R)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    try:
        path=path.replace('9','9_cc')
        f=open(path, 'w')
    except:
        print "int Create File error"

    p = np.array(w)/14.0
    for each in p:
        pg=r_perturbS(g, each)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    f.write(rstr)
    f.close()

def t_Gnutella_cc(path=r"d:\data\p2p-Gnutella08.txt"):
    rstr = ''
    g = nx.Graph()
    g = read_file_txt(g, path)
    w = [20777,18700,17995,17023]
    for each in w:
        R=gRa(g,each)
        pg=r_perturbR(g, R)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    try:
        path=path.replace('p2p-Gnutella','GrQcp2p-Gnutella_cc')
        f=open(path, 'w')
    except:
        print "int Create File error"

    p = np.array(w)/20777.0
    for each in p:
        pg=r_perturbS(g, each)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    f.write(rstr)
    f.close()

def t_facebook_cc(path=r"d:\data\facebook1.txt"):
    rstr = ''
    g = nx.Graph()
    g = read_file_txt(g, path)
    w = [1945, 1294, 860, 643]
    for each in w:
        R=gRa(g,each)
        pg=r_perturbR(g, R)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    try:
        path=path.replace('book1','book1_cc')
        f=open(path, 'w')
    except:
        print "int readFileTxt open error"

    p = np.array(w)/4813.0
    for each in p:
        pg=r_perturbS(g, each)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    f.write(rstr)
    f.close()

def compare_graphs(graph):
    n = nx.number_of_nodes(graph)
    m = nx.number_of_edges(graph)
    k = np.mean(list(nx.degree(graph).values()))
    erdos = nx.erdos_renyi_graph(n, p=m/float(n*(n-1)/2))
    barabasi = nx.barabasi_albert_graph(n, m=int(k)-7)
    small_world = nx.watts_strogatz_graph(n, int(k), p=0.04)
    print(' ')
    print('Compare the number of edges')
    print(' ')
    print('My network: ' + str(nx.number_of_edges(graph)))
    print('Erdos: ' + str(nx.number_of_edges(erdos)))
    print('Barabasi: ' + str(nx.number_of_edges(barabasi)))
    print('SW: ' + str(nx.number_of_edges(small_world)))
    print(' ')
    print('Compare average clustering coefficients')
    print(' ')
    print('My network: ' + str(nx.average_clustering(graph)))
    print('Erdos: ' + str(nx.average_clustering(erdos)))
    print('Barabasi: ' + str(nx.average_clustering(barabasi)))
    print('SW: ' + str(nx.average_clustering(small_world)))
    print(' ')
    print('Compare average path length')
    print(' ')
    print('My network: ' + str(nx.average_shortest_path_length(graph)))
    print('Erdos: ' + str(nx.average_shortest_path_length(erdos)))
    print('Barabasi: ' + str(nx.average_shortest_path_length(barabasi)))
    print('SW: ' + str(nx.average_shortest_path_length(small_world)))
    print(' ')
    print('Compare graph diameter')
    print(' ')
    print('My network: ' + str(nx.diameter(graph)))
    print('Erdos: ' + str(nx.diameter(erdos)))
    print('Barabasi: ' + str(nx.diameter(barabasi)))
    print('SW: ' + str(nx.diameter(small_world)))

def Type2AlmostCompleteGraph(n, m):
    if (BinomialCoefficient(n - 2, 2) + 4 <= m) and (m <= BinomialCoefficient(n - 1, 2) + 1):
        first_candidate = nx.complete_graph(n - 2)
        remaining_edges = m - BinomialCoefficient(n - 2, 2)
        first_candidate.add_edge(n - 2, 0)
        first_candidate.add_edge(n - 2, 1)
        for vertex_index in range(remaining_edges - 2):
            first_candidate.add_edge(n - 1, vertex_index)
        first_coefficient = nx.average_clustering(first_candidate)

        second_candidate = nx.complete_graph(n - 2)
        second_candidate.add_edge(n - 2, n - 1)
        remaining_edges = m - BinomialCoefficient(n - 2, 2) - 1
        number_of_common_neighbors = remaining_edges / 2
        for vertex_index in range(number_of_common_neighbors):
            second_candidate.add_edge(vertex_index, n - 2)
            second_candidate.add_edge(vertex_index, n - 1)
        if (remaining_edges - 2 * number_of_common_neighbors) == 1:
            second_candidate.add_edge(vertex_index + 1, n - 2)
        second_coefficient = nx.average_clustering(second_candidate)

        if first_coefficient > second_coefficient:
            G = first_candidate.copy()
        else:
            G = second_candidate.copy()
        return G

def can_combine_cluster2(cl1, cl2):
    combine = False
    temp_graph1 = G.subgraph(cl1)
    temp_graph2 = G.subgraph(cl2)
    temp_graph_all = G.subgraph(cl1 + cl2)
    
    if len(cl1) >= len(cl2):
        common_elements = list(set(cl1).intersection(set(cl2)))
        if len(common_elements) > 0.8*len(cl2):
            combine = True
        #print common_elements
    else:
        common_elements = list(set(cl2).intersection(set(cl1)))
        if len(common_elements) > 0.8*len(cl1): 
            combine = True


    clustering_coeff_1   = nx.average_clustering(temp_graph1)
    clustering_coeff_2   = nx.average_clustering(temp_graph2)
    clustering_coeff_all = nx.average_clustering(temp_graph_all)
    #print cl1
    #print cl2
    #print (str)(clustering_coeff_1) + " " + (str)(clustering_coeff_2) +" "+ (str)(clustering_coeff_all)
    #print " "
    
    if combine:
        if (clustering_coeff_all >= .8*clustering_coeff_1) and (clustering_coeff_all >= 0.8*clustering_coeff_2):
            return True
    else:
        if (clustering_coeff_all >= clustering_coeff_1) and (clustering_coeff_all >= clustering_coeff_2):
            return True
    return False 

def getCoherenceMeasure(essay):
	graph = makeWordGraph(essay)
	# obtain clustering coefficient			
	clustCoeffList=nx.clustering(graph)
	#####
	print getScore(clustCoeffList,graph)
	print nx.average_clustering(graph)

def algorithm(w1,w2,w3,w4,G1,G2,G3,G4):
	try:
		cc=np.array([nx.average_clustering(G1,weight='weight'),nx.average_clustering(G2,weight='weight'),nx.average_clustering(G3,weight='weight'),nx.average_clustering(G4,weight='weight')])
		spl=np.array([nx.average_shortest_path_length(G1,weight='weight'),nx.average_shortest_path_length(G2,weight='weight'),nx.average_shortest_path_length(G3,weight='weight'),nx.average_shortest_path_length(G4,weight='weight')])
		nds=np.array([nx.number_of_nodes(G1),nx.number_of_nodes(G2),nx.number_of_nodes(G3),nx.number_of_nodes(G4)])
		edgs= np.array([nx.number_of_edges(G1),nx.number_of_edges(G2),nx.number_of_edges(G3),nx.number_of_edges(G4)])
		if valid(cc):
			cc=stats.zscore(cc)
		else:
			cc=np.array([.1,.1,.1,.1])
		cc= cc-min(cc)+.1
		if valid(spl):
			spl=stats.zscore(spl)
		else:
			spl=np.array([.1,.1,.1,.1])
		spl= spl-min(spl)+.1
		if valid(nds):
			nds=stats.zscore(nds)
		else:
			nds=np.array([.1,.1,.1,.1])
		nds = nds-min(nds)+.1
		if valid(edgs):
			edgs=stats.zscore(edgs)
		else:
			edgs=np.array([.1,.1,.1,.1])
		edgs=edgs-min(edgs)+.1
		r1=(w1*cc[0]+w2*spl[0]+w3*nds[0]+w4*edgs[0])*1000
		r2=(w1*cc[1]+w2*spl[1]+w3*nds[1]+w4*edgs[1])*1000
		r3=(w1*cc[2]+w2*spl[2]+w3*nds[2]+w4*edgs[2])*1000
		r4=(w1*cc[3]+w2*spl[3]+w3*nds[3]+w4*edgs[3])*1000
		d={'Player 1:': r1, 'Player 2:': r2,'Player 3:': r3, 'Player 4:': r4}
		rank = sorted(d.items(), key=lambda x: x[1], reverse=True)
		return ["USAU RANKINGS",str(rank[0][0])+ " " + str(int(rank[0][1])),str(rank[1][0])+" "+ str(int(rank[1][1])),str(rank[2][0])+" "+ str(int(rank[2][1])),str(rank[3][0])+" "+str(int(rank[3][1]))]
	except:
		return ["Unable to compute rankings!  Need data","Player 1","Player 2","Player 3","Player 4"]

def t_GrQc_cc(path=r"d:\data\CA-GrQc.txt"):
    rstr = ''
    g = nx.Graph()
    g = read_file_txt(g, path)
    w = [14496,13454,12394,9782]
    for each in w:
        R=gRa(g,each)
        pg=r_perturbR(g, R)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    try:
        path=path.replace('GrQc','GrQc_cc')
        f=open(path, 'w')
    except:
        print "int readFileTxt open error"

    p = np.array(w)/14496.0
    for each in p:
        pg=r_perturbS(g, each)
        rstr=rstr+'{0:8},{1:10.4}'.format(each,nx.average_clustering(pg))
        rstr=rstr+'\n'

    f.write(rstr)
    f.close()

def test_clustering(size):
    print("Barabasi-Albert:")
    ba = networkx.barabasi_albert_graph(1000, 4)
    print("Clustering: ", networkx.average_clustering(ba))
    print("Average length: ", networkx.average_shortest_path_length(ba))
    print("Watts-Strogatz:")
    ws = networkx.watts_strogatz_graph(size, 4, 0.001)
    print("Clustering: ", networkx.average_clustering(ws))
    print("Average length: ", networkx.average_shortest_path_length(ws))

def gen_graph_stats (graph):
	G = nx.read_graphml(graph)
	stats = {}

	edges, nodes = 0,0
	for e in G.edges_iter(): edges += 1
	for n in G.nodes_iter(): nodes += 1
	stats['Edges'] = (edges,'The number of edges within the Graph')
	stats['Nodes'] = (nodes, 'The number of nodes within the Graph')
	print "%i edges, %i nodes" % (edges, nodes)


	# Accessing the highest degree node
	center, degree = sorted(G.degree().items(), key=itemgetter(1), reverse=True)[0]
	stats['Center Node'] = ('%s: %0.5f' % (center,degree),'The center most node in the graph. Which has the highest degree')


	hairball = nx.subgraph(G, [x for x in nx.connected_components(G)][0])
	print "Average shortest path: %0.4f" % nx.average_shortest_path_length(hairball)
	stats['Average Shortest Path Length'] = (nx.average_shortest_path_length(hairball), '')
	# print "Center: %s" % G[center]

	# print "Shortest Path to Center: %s" % p


	print "Degree: %0.5f" % degree
	stats['Degree'] = (degree,'The node degree is the number of edges adjacent to that node.')

	print "Order: %i" % G.number_of_nodes()
	stats['Order'] = (G.number_of_nodes(),'The number of nodes in the graph.')

	print "Size: %i" % G.number_of_edges()
	stats['Size'] = (G.number_of_edges(),'The number of edges in the graph.')

	print "Clustering: %0.5f" % nx.average_clustering(G)
	stats['Average Clustering'] = (nx.average_clustering(G),'The average clustering coefficient for the graph.')

	print "Transitivity: %0.5f" % nx.transitivity(G)
	stats['Transitivity'] = (nx.transitivity(G),'The fraction of all possible triangles present in the graph.')

	part = community.best_partition(G)
	# values = [part.get(node) for node in G.nodes()]

	# nx.draw_spring(G, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=False)
	# plt.show()

	mod = community.modularity(part,G)
	print "modularity: %0.5f" % mod
	stats['Modularity'] = (mod,'The modularity of a partition of a graph.')

	knn = nx.k_nearest_neighbors(G)
	print knn
	stats['K Nearest Neighbors'] = (knn,'the average degree connectivity of graph.\nThe average degree connectivity is the average nearest neighbor degree of nodes with degree k. For weighted graphs, an analogous measure can be computed using the weighted average neighbors degre')


	return G, stats