Python networkx.configuration_model方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def generate_network(Pk, N, ntries = 100):
    r'''Generates an N-node random network whose degree distribution is given by Pk'''
    counter = 0
    while counter< ntries:
        counter += 1
        ks = []
        for ctr in range(N):
            ks.append(Pk())
        if sum(ks)%2 == 0:
            break
    if sum(ks)%2 ==1:
        raise EoN.EoNError("cannot generate even degree sum")
    G = nx.configuration_model(ks)
    return G
 


#An erdos-renyi network has a Poisson degree distribution. 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_SIS_simulations(self):
        print("test_SIS_simulations")
        plt.clf()
        tau = 0.1
        gamma = 0.3
        G = nx.configuration_model([1, 5, 10] * 100000)
        G = nx.Graph(G)
        G.remove_edges_from(G.selfloop_edges())
        N = G.order()
        initial_size = 5000
        for counter in range(10):
            print('fast_SIS')
            t, S, I = EoN.fast_SIS(G, tau, gamma, initial_infecteds=range(initial_size), tmax=20)
            plt.plot(t, S, '-.', color='b', alpha=0.3)
            plt.plot(t, I, '-.', color='b', alpha=0.3)
            print('Gillespie_SIS')
            t, S, I = EoN.Gillespie_SIS(G, tau, gamma, initial_infecteds=range(initial_size), tmax=20)
            plt.plot(t, S, '--', color='r', alpha=0.3)
            plt.plot(t, I, '--', color='r', alpha=0.3)
            plt.title('curves should overlie to show event driven and gillespie agree')
            plt.savefig('SIS_sims') 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def EBCM_pref_mix_discrete_from_graph(G, p, rho = None, tmin = 0, tmax = 100, return_full_data=False):
    
    '''
    Takes a given graph, finds degree correlations, and calls EBCM_pref_mix_discrete
    
    :SAMPLE USE:
        
    ::

        import networkx as nx
        import EoN
        import matplotlib.pyplot as plt
        G = nx.bipartite.configuration_model([5]*300000, [2]*750000)
        t, S, I, R = EoN.basic_discrete_SIR(G, 0.6, rho = 0.002)
        tx, Sx, Ix, Rx = EoN.EBCM_pref_mix_discrete_from_graph(G, 0.6, rho=0.002, tmax=t[-1])
        plt.plot(t, I, label = 'simulation')
        plt.plot(tx, Ix, '--', label = 'analytic prediction')
        plt.legend(loc='upper right')
        plt.show()
    '''
            
    N = G.order()
    Pk = get_Pk(G)
    Pnk = get_Pnk(G)
    return EBCM_pref_mix_discrete(N, Pk, Pnk, p, rho=rho, tmin=tmin, tmax=tmax, return_full_data=return_full_data) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_havel_hakimi_construction():
    G = nx.havel_hakimi_graph([])
    assert_equal(len(G), 0)

    z = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
    assert_raises(nx.NetworkXError, nx.havel_hakimi_graph, z)
    z = ["A", 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
    assert_raises(nx.NetworkXError, nx.havel_hakimi_graph, z)

    z = [5, 4, 3, 3, 3, 2, 2, 2]
    G = nx.havel_hakimi_graph(z)
    G = nx.configuration_model(z)
    z = [6, 5, 4, 4, 2, 1, 1, 1]
    assert_raises(nx.NetworkXError, nx.havel_hakimi_graph, z)

    z = [10, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2]

    G = nx.havel_hakimi_graph(z)

    assert_raises(nx.NetworkXError, nx.havel_hakimi_graph, z,
                  create_using=nx.DiGraph()) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def get_G(N, Pk):
    while True:
        ks = []
        for ctr in range(N):
            r = random.random()
            for k in Pk:
                if r<Pk[k]:
                    break
                else:
                    r-= Pk[k]
            ks.append(k)
        if sum(ks)%2==0:
            break
    G = nx.configuration_model(ks)
    return G 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def regular_graph_generation(N, kave):
    return nx.configuration_model([kave]*N) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def generateFrom(N, p, maxdeg=100):
    # construct degrees according to the distribution given
    # by the model function
    ns = []
    t = 0
    for i in range(N):
        while True:
            k = rng.integers(1, maxdeg)
            if rng.random() < p(k):
                ns.append(k)
                t += k
                break

    # the final sequence of degrees has to sum to an even number, as
    # each edge has two endpoints
    # if the sequence is odd, remove an element and draw another from
    # the distribution, repeating until the overall sequence is even
    while t % 2 != 0:
        # pick a node at random
        i = rng.integers(0, len(ns) - 1)

        # remove it from the sequence and from the total
        t -= ns[i]
        del ns[i]
            
        # draw a new degree from the distribution
        while True:
            k = rng.integers(1, maxdeg)
            if rng.random() < p(k):
                # add new node to the sequence
                ns.append(k)
                t += k
                break

    # populate the network using the configuration
    # model with the given degree distribution
    g = networkx.configuration_model(ns, create_using=networkx.Graph())
    return g 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_empty_degree_sequence(self):
        """Tests that an empty degree sequence yields the null graph."""
        G = nx.configuration_model([])
        assert_equal(len(G), 0) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_degree_zero(self):
        """Tests that a degree sequence of all zeros yields the empty
        graph.

        """
        G = nx.configuration_model([0, 0, 0])
        assert_equal(len(G), 3)
        assert_equal(G.number_of_edges(), 0) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_degree_sequence(self):
        """Tests that the degree sequence of the generated graph matches
        the input degree sequence.

        """
        deg_seq = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
        G = nx.configuration_model(deg_seq, seed=12345678)
        assert_equal(sorted((d for n, d in G.degree()), reverse=True),
                     [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1])
        assert_equal(sorted((d for n, d in G.degree(range(len(deg_seq)))),
                            reverse=True),
                     [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_random_seed(self):
        """Tests that each call with the same random seed generates the
        same graph.

        """
        deg_seq = [3] * 12
        G1 = nx.configuration_model(deg_seq, seed=1000)
        G2 = nx.configuration_model(deg_seq, seed=1000)
        assert_true(nx.is_isomorphic(G1, G2))
        G1 = nx.configuration_model(deg_seq, seed=10)
        G2 = nx.configuration_model(deg_seq, seed=10)
        assert_true(nx.is_isomorphic(G1, G2)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_odd_degree_sum(self):
        """Tests that a degree sequence whose sum is odd yields an
        exception.

        """
        nx.configuration_model([1, 2]) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def test_Gillespie_complex_contagion(self):
        def transition_rate(G, node, status, parameters):
            # this function needs to return the rate at which ``node`` changes status
            #
            r = parameters[0]
            if status[node] == 'S' and len([nbr for nbr in G.neighbors(node) if status[nbr] == 'I']) > 1:
                return 1
            else:  # status[node] might be 0 or length might be 0 or 1.
                return 0

        def transition_choice(G, node, status, parameters):
            # this function needs to return the new status of node.  We assume going
            # in that we have already calculated it is changing status.
            #
            # this function could be more elaborate if there were different
            # possible transitions that could happen.  However, for this model,
            # the 'I' nodes aren't changing status, and the 'S' ones are changing to 'I'
            # So if we're in this function, the node must be 'S' and becoming 'I'
            #
            return 'I'

        def get_influence_set(G, node, status, parameters):
            # this function needs to return any node whose rates might change
            # because ``node`` has just changed status.  That is, which nodes
            # might ``node`` influence?
            #
            # For our models the only nodes a node might affect are the susceptible neighbors.

            return {nbr for nbr in G.neighbors(node) if status[nbr] == 'S'}

        parameters = (2,)  # this is the threshold.  Note the comma.  It is needed
        # for python to realize this is a 1-tuple, not just a number.
        # ``parameters`` is sent as a tuple so we need the comma.

        N = 60000
        deg_dist = [2, 4, 6] * int(N / 3)
        G = nx.configuration_model(deg_dist)

        for rho in np.linspace(3. / 80, 7. / 80, 8):  # 8 values from 3/80 to 7/80.
            print(rho)
            IC = defaultdict(lambda: 'S')
            for node in G.nodes():
                if np.random.random() < rho:  # there are faster ways to do this random selection
                    IC[node] = 'I'

            t, S, I = EoN.Gillespie_complex_contagion(G, transition_rate, transition_choice,
                                                      get_influence_set, IC, return_statuses=('S', 'I'),
                                                      parameters=parameters)

            plt.plot(t, I)

        plt.savefig('test_Gillespie_complex_contagion') 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def random_degree_sequence_graph(sequence, seed=None, tries=10):
    r"""Return a simple random graph with the given degree sequence.

    If the maximum degree `d_m` in the sequence is `O(m^{1/4})` then the
    algorithm produces almost uniform random graphs in `O(m d_m)` time
    where `m` is the number of edges.

    Parameters
    ----------
    sequence :  list of integers
        Sequence of degrees
    seed : hashable object, optional
        Seed for random number generator
    tries : int, optional
        Maximum number of tries to create a graph

    Returns
    -------
    G : Graph
        A graph with the specified degree sequence.
        Nodes are labeled starting at 0 with an index
        corresponding to the position in the sequence.

    Raises
    ------
    NetworkXUnfeasible
        If the degree sequence is not graphical.
    NetworkXError
        If a graph is not produced in specified number of tries

    See Also
    --------
    is_valid_degree_sequence, configuration_model

    Notes
    -----
    The generator algorithm [1]_ is not guaranteed to produce a graph.

    References
    ----------
    .. [1] Moshen Bayati, Jeong Han Kim, and Amin Saberi,
       A sequential algorithm for generating random graphs.
       Algorithmica, Volume 58, Number 4, 860-910,
       DOI: 10.1007/s00453-009-9340-1

    Examples
    --------
    >>> sequence = [1, 2, 2, 3]
    >>> G = nx.random_degree_sequence_graph(sequence)
    >>> sorted(G.degree().values())
    [1, 2, 2, 3]
    """
    DSRG = DegreeSequenceRandomGraph(sequence, seed=seed)
    for try_n in range(tries):
        try:
            return DSRG.generate()
        except nx.NetworkXUnfeasible:
            pass
    raise nx.NetworkXError('failed to generate graph in %d tries'%tries) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import configuration_model [as 别名]
def random_degree_sequence_graph(sequence, seed=None, tries=10):
    r"""Returns a simple random graph with the given degree sequence.

    If the maximum degree $d_m$ in the sequence is $O(m^{1/4})$ then the
    algorithm produces almost uniform random graphs in $O(m d_m)$ time
    where $m$ is the number of edges.

    Parameters
    ----------
    sequence :  list of integers
        Sequence of degrees
    seed : integer, random_state, or None (default)
        Indicator of random number generation state.
        See :ref:`Randomness<randomness>`.
    tries : int, optional
        Maximum number of tries to create a graph

    Returns
    -------
    G : Graph
        A graph with the specified degree sequence.
        Nodes are labeled starting at 0 with an index
        corresponding to the position in the sequence.

    Raises
    ------
    NetworkXUnfeasible
        If the degree sequence is not graphical.
    NetworkXError
        If a graph is not produced in specified number of tries

    See Also
    --------
    is_graphical, configuration_model

    Notes
    -----
    The generator algorithm [1]_ is not guaranteed to produce a graph.

    References
    ----------
    .. [1] Moshen Bayati, Jeong Han Kim, and Amin Saberi,
       A sequential algorithm for generating random graphs.
       Algorithmica, Volume 58, Number 4, 860-910,
       DOI: 10.1007/s00453-009-9340-1

    Examples
    --------
    >>> sequence = [1, 2, 2, 3]
    >>> G = nx.random_degree_sequence_graph(sequence)
    >>> sorted(d for n, d in G.degree())
    [1, 2, 2, 3]
    """
    DSRG = DegreeSequenceRandomGraph(sequence, seed)
    for try_n in range(tries):
        try:
            return DSRG.generate()
        except nx.NetworkXUnfeasible:
            pass
    raise nx.NetworkXError('failed to generate graph in %d tries' % tries)