Python networkx.is_connected方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def generate_scalefree_graph(variables_count, m_edge, allow_subgraph):
    if not allow_subgraph:
        graph = nx.barabasi_albert_graph(variables_count, m_edge)
        is_connected = nx.is_connected(graph)
        while not is_connected:
            graph = nx.barabasi_albert_graph(variables_count, m_edge)
            is_connected = nx.is_connected(graph)
    else:
        graph = nx.barabasi_albert_graph(variables_count, m_edge)

    # In the obtained graph, low rank nodes will have a much higher edge count
    # than high rank nodes. We shuffle the nodes names to avoid this effect:
    new_nodes = list(range(variables_count))
    random.shuffle(new_nodes)
    node_mapping = {n: nn for n, nn in zip(graph.nodes, new_nodes)}

    new_graph = nx.Graph((node_mapping[e1], node_mapping[e2]) for e1, e2 in graph.edges)
    return new_graph 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def decode_graph(adj, prefix):
    adj = np.asmatrix(adj)
    G = nx.from_numpy_matrix(adj)
    # G.remove_nodes_from(nx.isolates(G))
    print('num of nodes: {}'.format(G.number_of_nodes()))
    print('num of edges: {}'.format(G.number_of_edges()))
    G_deg = nx.degree_histogram(G)
    G_deg_sum = [a * b for a, b in zip(G_deg, range(0, len(G_deg)))]
    print('average degree: {}'.format(sum(G_deg_sum) / G.number_of_nodes()))
    if nx.is_connected(G):
        print('average path length: {}'.format(nx.average_shortest_path_length(G)))
        print('average diameter: {}'.format(nx.diameter(G)))
    G_cluster = sorted(list(nx.clustering(G).values()))
    print('average clustering coefficient: {}'.format(sum(G_cluster) / len(G_cluster)))
    cycle_len = []
    cycle_all = nx.cycle_basis(G, 0)
    for item in cycle_all:
        cycle_len.append(len(item))
    print('cycles', cycle_len)
    print('cycle count', len(cycle_len))
    draw_graph(G, prefix=prefix) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def dendritic_graph(self):
        """
        Builds skeleton of the topological representation (used internally)
        """
        diam = networkx.diameter(self.gl)
        g3 = networkx.Graph()
        dicdend = {}
        for n in range(diam-1):
            nodedist = []
            for k in self.pl:
                dil = networkx.shortest_path_length(self.gl, self.root, k)
                if dil == n:
                    nodedist.append(str(k))
            g2 = self.gl.subgraph(nodedist)
            dicdend[n] = sorted(networkx.connected_components(g2))
            for n2, yu in enumerate(dicdend[n]):
                g3.add_node(str(n) + '_' + str(n2))
                if n > 0:
                    for n3, yu2 in enumerate(dicdend[n-1]):
                        if networkx.is_connected(self.gl.subgraph(list(yu)+list(yu2))):
                            g3.add_edge(str(n) + '_' + str(n2), str(n-1) + '_' + str(n3))
        return g3, dicdend 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def validate_cuts(G, s, t, solnValue, partition, capacity, flow_func):
    assert_true(all(n in G for n in partition[0]),
                msg=msg.format(flow_func.__name__))
    assert_true(all(n in G for n in partition[1]),
                msg=msg.format(flow_func.__name__))
    cutset = compute_cutset(G, partition)
    assert_true(all(G.has_edge(u, v) for (u, v) in cutset),
                msg=msg.format(flow_func.__name__))
    assert_equal(solnValue, sum(G[u][v][capacity] for (u, v) in cutset),
                msg=msg.format(flow_func.__name__))
    H = G.copy()
    H.remove_edges_from(cutset)
    if not G.is_directed():
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
    else:
        assert_false(nx.is_strongly_connected(H),
                     msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def test_petersen_cutset():
    G = nx.petersen_graph()
    for flow_func in flow_funcs:
        kwargs = dict(flow_func=flow_func)
        # edge cuts
        edge_cut = nx.minimum_edge_cut(G, **kwargs)
        assert_equal(3, len(edge_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_edges_from(edge_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
        # node cuts
        node_cut = nx.minimum_node_cut(G, **kwargs)
        assert_equal(3, len(node_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_nodes_from(node_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def test_icosahedral_cutset():
    G=nx.icosahedral_graph()
    for flow_func in flow_funcs:
        kwargs = dict(flow_func=flow_func)
        # edge cuts
        edge_cut = nx.minimum_edge_cut(G, **kwargs)
        assert_equal(5, len(edge_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_edges_from(edge_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
        # node cuts
        node_cut = nx.minimum_node_cut(G, **kwargs)
        assert_equal(5, len(node_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_nodes_from(node_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def validate_cuts(G, s, t, solnValue, partition, capacity, flow_func):
    assert_true(all(n in G for n in partition[0]),
                msg=msg.format(flow_func.__name__))
    assert_true(all(n in G for n in partition[1]),
                msg=msg.format(flow_func.__name__))
    cutset = compute_cutset(G, partition)
    assert_true(all(G.has_edge(u, v) for (u, v) in cutset),
                msg=msg.format(flow_func.__name__))
    assert_equal(solnValue, sum(G[u][v][capacity] for (u, v) in cutset),
                 msg=msg.format(flow_func.__name__))
    H = G.copy()
    H.remove_edges_from(cutset)
    if not G.is_directed():
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
    else:
        assert_false(nx.is_strongly_connected(H),
                     msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def test_icosahedral_cutset():
    G = nx.icosahedral_graph()
    for flow_func in flow_funcs:
        kwargs = dict(flow_func=flow_func)
        # edge cuts
        edge_cut = nx.minimum_edge_cut(G, **kwargs)
        assert_equal(5, len(edge_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_edges_from(edge_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
        # node cuts
        node_cut = nx.minimum_node_cut(G, **kwargs)
        assert_equal(5, len(node_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_nodes_from(node_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def test_octahedral_cutset():
    G=nx.octahedral_graph()
    for flow_func in flow_funcs:
        kwargs = dict(flow_func=flow_func)
        # edge cuts
        edge_cut = nx.minimum_edge_cut(G, **kwargs)
        assert_equal(4, len(edge_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_edges_from(edge_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
        # node cuts
        node_cut = nx.minimum_node_cut(G, **kwargs)
        assert_equal(4, len(node_cut), msg=msg.format(flow_func.__name__))
        H = G.copy()
        H.remove_nodes_from(node_cut)
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def generate_random_graph(variables_count, p_edge, allow_subgraph):
    if not allow_subgraph:
        graph = nx.gnp_random_graph(variables_count, p_edge)
        is_connected = nx.is_connected(graph)
        while not is_connected:
            graph = nx.gnp_random_graph(variables_count, p_edge)
            is_connected = nx.is_connected(graph)
    else:
        graph = nx.gnp_random_graph(variables_count, p_edge)
    return graph 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def _check_connectivity(self, graph):
        """Checking the connected nature of a single graph."""
        connected = nx.is_connected(graph)
        assert connected, "Graph is not connected." 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def is_k_edge_connected(G, k):
    """
    Tests to see if a graph is k-edge-connected

    See Also
    --------
    is_locally_k_edge_connected

    Example
    -------
    >>> G = nx.barbell_graph(10, 0)
    >>> is_k_edge_connected(G, k=1)
    True
    >>> is_k_edge_connected(G, k=2)
    False
    """
    if k < 1:
        raise ValueError('k must be positive, not {}'.format(k))
    # First try to quickly determine if G is not k-edge-connected
    if G.number_of_nodes() < k + 1:
        return False
    elif any(d < k for n, d in G.degree()):
        return False
    else:
        # Otherwise perform the full check
        if k == 1:
            return nx.is_connected(G)
        elif k == 2:
            return not nx.has_bridges(G)
        else:
            # return nx.edge_connectivity(G, cutoff=k) >= k
            return nx.edge_connectivity(G) >= k 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def weighted_one_edge_augmentation(G, avail, weight=None, partial=False):
    """Finds the minimum weight set of edges to connect G if one exists.

    This is a variant of the weighted MST problem.

    Example
    -------
    >>> G = nx.Graph([(1, 2), (2, 3), (4, 5)])
    >>> G.add_nodes_from([6, 7, 8])
    >>> # any edge not in avail has an implicit weight of infinity
    >>> avail = [(1, 3), (1, 5), (4, 7), (4, 8), (6, 1), (8, 1), (8, 2)]
    >>> sorted(weighted_one_edge_augmentation(G, avail))
    [(1, 5), (4, 7), (6, 1), (8, 1)]
    >>> # find another solution by giving large weights to edges in the
    >>> # previous solution (note some of the old edges must be used)
    >>> avail = [(1, 3), (1, 5, 99), (4, 7, 9), (6, 1, 99), (8, 1, 99), (8, 2)]
    >>> sorted(weighted_one_edge_augmentation(G, avail))
    [(1, 5), (4, 7), (6, 1), (8, 2)]
    """
    avail_uv, avail_w = _unpack_available_edges(avail, weight=weight, G=G)
    # Collapse CCs in the original graph into nodes in a metagraph
    # Then find an MST of the metagraph instead of the original graph
    C = collapse(G, nx.connected_components(G))
    mapping = C.graph['mapping']
    # Assign each available edge to an edge in the metagraph
    candidate_mapping = _lightest_meta_edges(mapping, avail_uv, avail_w)
    # nx.set_edge_attributes(C, name='weight', values=0)
    C.add_edges_from(
        (mu, mv, {'weight': w, 'generator': uv})
        for (mu, mv), uv, w in candidate_mapping
    )
    # Find MST of the meta graph
    meta_mst = nx.minimum_spanning_tree(C)
    if not partial and not nx.is_connected(meta_mst):
        raise nx.NetworkXUnfeasible(
            'Not possible to connect G with available edges')
    # Yield the edge that generated the meta-edge
    for mu, mv, d in meta_mst.edges(data=True):
        if 'generator' in d:
            edge = d['generator']
            yield edge 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def _check_inconsistency(infr, nid, cc=None):
        """
        Check if a PCC contains an error
        """
        if cc is None:
            cc = infr.pos_graph.component(nid)
        was_clean = infr._purge_error_edges(nid)
        neg_edges = list(nxu.edges_inside(infr.neg_graph, cc))
        if neg_edges:
            pos_subgraph_ = infr.pos_graph.subgraph(cc, dynamic=False).copy()
            if not nx.is_connected(pos_subgraph_):
                print('cc = %r' % (cc,))
                print('pos_subgraph_ = %r' % (pos_subgraph_,))
                raise AssertionError('must be connected')
            hypothesis = dict(infr.hypothesis_errors(pos_subgraph_, neg_edges))
            assert len(hypothesis) > 0, 'must have at least one'
            infr._set_error_edges(nid, set(hypothesis.keys()))
            is_clean = False
        else:
            infr.recover_graph.remove_nodes_from(cc)
            num = infr.recover_graph.number_of_components()
            # num = len(list(nx.connected_components(infr.recover_graph)))
            msg = ('An inconsistent PCC recovered, '
                   '{} inconsistent PCC(s) remain').format(num)
            infr.print(msg, 2, color='green')
            infr.update_pos_redun(nid, force=True)
            infr.update_extern_neg_redun(nid, force=True)
            is_clean = True
        return (was_clean, is_clean) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_connected [as 别名]
def hypothesis_errors(infr, pos_subgraph, neg_edges):
        if not nx.is_connected(pos_subgraph):
            raise AssertionError('Not connected' + repr(pos_subgraph))
        infr.print(
            'Find hypothesis errors in {} nodes with {} neg edges'.format(
                len(pos_subgraph), len(neg_edges)), 3)

        pos_edges = list(pos_subgraph.edges())

        neg_weight = infr._mincut_edge_weights(neg_edges)
        pos_weight = infr._mincut_edge_weights(pos_edges)

        capacity = 'weight'
        nx.set_edge_attributes(pos_subgraph, name=capacity, values=ut.dzip(pos_edges, pos_weight))

        # Solve a multicut problem for multiple pairs of terminal nodes.
        # Running multiple min-cuts produces a k-factor approximation
        maybe_error_edges = set([])
        for (s, t), join_weight in zip(neg_edges, neg_weight):
            cut_weight, parts = nx.minimum_cut(pos_subgraph, s, t,
                                               capacity=capacity)
            cut_edgeset = nxu.edges_cross(pos_subgraph, *parts)
            if join_weight < cut_weight:
                join_edgeset = {(s, t)}
                chosen = join_edgeset
                hypothesis = POSTV
            else:
                chosen = cut_edgeset
                hypothesis = NEGTV
            for edge in chosen:
                if edge not in maybe_error_edges:
                    maybe_error_edges.add(edge)
                    yield (edge, hypothesis)