示例#1
0
def barabasi_albert(n,
                    n_init,
                    k=1,
                    node_names=None,
                    directed=False,
                    temporal=False):
    """
    """

    if node_names is None:
        node_names = [str(x) for x in range(n)]
    assert len(
        node_names
    ) >= n, 'Error: Number of node names not matching degree sequence length'
    if not temporal:
        network = Network(directed=directed)
    else:
        network = TemporalNetwork()

    endpoints = []
    time = 0

    # initial network
    for i in range(n_init):
        for j in range(n_init):
            if i < j:
                if not temporal:
                    network.add_edge(str(node_names[i]), str(node_names[j]))
                else:
                    network.add_edge(str(node_names[i]),
                                     str(node_names[j]),
                                     time,
                                     directed=directed)
                endpoints.append(str(node_names[i]))
                endpoints.append(str(node_names[j]))

    for i in range(n_init, n):
        time += 1
        # TODO: for k>1 we can choose different stubs of the same node in one step!
        targets = _np.random.choice(endpoints, size=k, replace=False)
        for t in targets:
            if not temporal:
                network.add_edge(str(node_names[i]), t)
            else:
                network.add_edge(str(node_names[i]),
                                 t,
                                 time,
                                 directed=directed)
            endpoints.append(str(node_names[i]))
            endpoints.append(t)
    return network
示例#2
0
def erdoes_renyi_gnm(n,
                     m,
                     node_names=None,
                     self_loops=True,
                     directed=False,
                     temporal=False):
    """
    """
    if node_names is None:
        node_names = [str(x) for x in range(n)]

    assert len(
        node_names
    ) >= n, 'Error: Number of node names not matching degree sequence length'

    if not temporal:
        network = Network(directed=directed)
    else:
        network = TemporalNetwork()

    # generate nodes
    if not temporal:
        for i in range(n):
            network.add_node(str(node_names[i]))

    time = -1
    m_current = 0
    edges = defaultdict(lambda: False)

    # add edges
    while m_current < m:
        edge = _np.random.choice(n, size=2, replace=self_loops)
        edge = [node_names[edge[0]], node_names[edge[1]]]
        if not edges[(edge[0], edge[1])]:
            edges[(edge[0], edge[1])] = True
            if not directed:
                edges[(edge[1], edge[0])] = True
            if not temporal:
                m_current += 1
                network.add_edge(edge[0], edge[1])
            else:
                time += 1
                m_current += 1
                network.add_edge(edge[0], edge[1], time, directed=directed)
    return network
示例#3
0
def erdoes_renyi_gnp(n,
                     p,
                     node_names=None,
                     self_loops=True,
                     directed=False,
                     temporal=False):
    """
    """
    if node_names is None:
        node_names = [str(x) for x in range(n)]
    assert len(
        node_names
    ) >= n, 'Error: Number of node names not matching degree sequence length'

    if not temporal:
        network = Network(directed=directed)
    else:
        network = TemporalNetwork()

    # make sure that isolated nodes exist
    if not temporal:
        for i in range(n):
            network.add_node(str(node_names[i]))

    time = -1

    # add edges
    for i in range(n):
        for j in range(i + 1):
            if i != j or self_loops:
                if _np.random.rand() <= p:
                    if not temporal:
                        network.add_edge(node_names[i], node_names[j])
                    else:
                        time += 1
                        network.add_edge(node_names[i],
                                         node_names[j],
                                         time,
                                         directed=directed)
    return network