HypDiff/Synthatic_graph_generator.py at main · RingBDStack/HypDiff · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138

import networkx as nx
import scipy
import numpy as np
from  plotter import  plotG
import numpy
from operator import itemgetter
import random


def Synthetic_data(type= "grid", rand = False):
    if rand==True:
        if type == "grid":
            G = grid(random.randint(10,15), random.randint(10,15))
        elif type == "community":
            G = n_community([50, 50], p_inter=0.05)
        elif type == "ego":
            G = ego()
        elif type == "lobster":
            G = lobster()
        elif type == "multi_rel_com":
            G = multi_rel_com()
    else:
        numpy.random.seed(4812)
        np.random.RandomState(1234)
        random.seed(245)

        if type == "grid":
            G = grid()
        elif type== "community":
            G = n_community([50,50,50,50], p_inter=0.05)
        elif type == "ego":
            G = ego()
        elif type=="lobster":
            G=lobster()
        elif type =="multi_rel_com":
            G = multi_rel_com()

        plotG(G, type)
    return nx.adjacency_matrix(G), scipy.sparse.lil_matrix(scipy.sparse.identity(G.number_of_nodes()))

def grid(m= 10, n=10 ):
    # https: // networkx.github.io / documentation / stable / auto_examples / drawing / plot_four_grids.html
    G = nx.grid_2d_graph(m, n)  # 4x4 grid
    return G

def n_community(c_sizes, p_inter=0.1, p_intera=0.4):
    graphs = [nx.gnp_random_graph(c_sizes[i], p_intera, seed=i) for i in range(len(c_sizes))]
    G = nx.disjoint_union_all(graphs)
    communities = list(nx.connected_components(G))
    for i in range(len(communities)):
        subG1 = communities[i]
        nodes1 = list(subG1)
        for j in range(i+1, len(communities)):
            subG2 = communities[j]
            nodes2 = list(subG2)
            has_inter_edge = False
            for n1 in nodes1:
                for n2 in nodes2:
                    if np.random.rand() < p_inter:
                        G.add_edge(n1, n2)
                        has_inter_edge = True
            if not has_inter_edge:
                G.add_edge(nodes1[0], nodes2[0])
    # print('connected comp: ', len(list(nx.connected_components(G))))
    return G

def multi_rel_com(comunities =[[50,50,50,50],  [100,100]], graph_size= 200):
    """

    :param comunities: a list of lists, in which each list determine a seet of communities and the size of each one,
    the inter and intera edge probablity will be random.
    :node_num  the graph size
    :return:
    """
    graphs = []
    for community in comunities:
        graphs.append(ncommunity(community, graph_size, random.uniform(.0001,.01), random.uniform(.2,.7)))

    H = nx.compose(graphs[0], graphs[1])
    for i in range(2, len(graphs)):
        H = nx.compose(H, graphs[i])
    return H


def ncommunity(c_sizes, graph_size, p_inter=0.1, p_intera=0.4 ):
        graphs = [nx.gnp_random_graph(c_sizes[i], p_intera, seed=i) for i in range(len(c_sizes))]
        G = nx.disjoint_union_all(graphs)
        communities = list(nx.connected_components(G))
        for i in range(len(communities)):
            subG1 = communities[i]
            nodes1 = list(subG1)
            for j in range(i + 1, len(communities)):
                subG2 = communities[j]
                nodes2 = list(subG2)
                has_inter_edge = False
                for n1 in nodes1:
                    for n2 in nodes2:
                        if np.random.rand() < p_inter:
                            G.add_edge(n1, n2)
                            has_inter_edge = True
                if not has_inter_edge:
                    G.add_edge(nodes1[0], nodes2[0])

        x = list(range(graph_size))
        random.shuffle(x)

        if(len(G)> graph_size):
            G.add_nodes_from([i for i in range(len(G), graph_size)])
        mapping = {k: v for k, v in zip(list(range(graph_size)), x)}
        G = nx.relabel_nodes(G, mapping)
        return G

def lobster():
    p1 = 0.7
    p2 = 0.7
    mean_node = 80
    G = nx.random_lobster(mean_node, p1, p2)
    return G

def ego():
    # Create a BA model graph
    n = 2000
    m = 3
    G = nx.generators.barabasi_albert_graph(n, m)
    # find node with largest degree
    node_and_degree = G.degree()
    (largest_hub, degree) = sorted(node_and_degree, key=itemgetter(1))[-1]
    # Create ego graph of main hub
    hub_ego = nx.ego_graph(G, largest_hub)
    return hub_ego


if __name__ == '__main__':
    Synthetic_data("multi_rel_com")
    print("closed")