Python networkx.star_graph方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_line_graph():
    N = 5
    G = dgl.DGLGraph(nx.star_graph(N))
    G.edata['h'] = F.randn((2 * N, D))
    n_edges = G.number_of_edges()
    L = G.line_graph(shared=True)
    assert L.number_of_nodes() == 2 * N
    L.ndata['h'] = F.randn((2 * N, D))
    # update node features on line graph should reflect to edge features on
    # original graph.
    u = [0, 0, 2, 3]
    v = [1, 2, 0, 0]
    eid = G.edge_ids(u, v)
    L.nodes[eid].data['h'] = F.zeros((4, D))
    assert F.allclose(G.edges[u, v].data['h'], F.zeros((4, D)))

    # adding a new node feature on line graph should also reflect to a new
    # edge feature on original graph
    data = F.randn((n_edges, D))
    L.ndata['w'] = data
    assert F.allclose(G.edata['w'], data) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_neighbors(self):
        graph = nx.complete_graph(100)
        pop = random.sample(graph.nodes(), 1)
        nbors = list(nx.neighbors(graph, pop[0]))
        # should be all the other vertices in the graph
        assert_equal(len(nbors), len(graph) - 1)

        graph = nx.path_graph(100)
        node = random.sample(graph.nodes(), 1)[0]
        nbors = list(nx.neighbors(graph, node))
        # should be all the other vertices in the graph
        if node != 0 and node != 99:
            assert_equal(len(nbors), 2)
        else:
            assert_equal(len(nbors), 1)

        # create a star graph with 99 outer nodes
        graph = nx.star_graph(99)
        nbors = list(nx.neighbors(graph, 0))
        assert_equal(len(nbors), 99) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_non_edges(self):
        # All possible edges exist
        graph = nx.complete_graph(5)
        nedges = list(nx.non_edges(graph))
        assert_equal(len(nedges), 0)

        graph = nx.path_graph(4)
        expected = [(0, 2), (0, 3), (1, 3)]
        nedges = list(nx.non_edges(graph))
        for (u, v) in expected:
            assert_true( (u, v) in nedges or (v, u) in nedges )

        graph = nx.star_graph(4)
        expected = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
        nedges = list(nx.non_edges(graph))
        for (u, v) in expected:
            assert_true( (u, v) in nedges or (v, u) in nedges )

        # Directed graphs
        graph = nx.DiGraph()
        graph.add_edges_from([(0, 2), (2, 0), (2, 1)])
        expected = [(0, 1), (1, 0), (1, 2)]
        nedges = list(nx.non_edges(graph))
        for e in expected:
            assert_true(e in nedges) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_threshold_sequence_graph_test(self):
        G=nx.star_graph(10)
        assert_true(nxt.is_threshold_graph(G))
        assert_true(nxt.is_threshold_sequence(list(G.degree().values())))

        G=nx.complete_graph(10)
        assert_true(nxt.is_threshold_graph(G))
        assert_true(nxt.is_threshold_sequence(list(G.degree().values())))

        deg=[3,2,2,1,1,1]
        assert_false(nxt.is_threshold_sequence(deg))

        deg=[3,2,2,1]
        assert_true(nxt.is_threshold_sequence(deg))

        G=nx.generators.havel_hakimi_graph(deg)
        assert_true(nxt.is_threshold_graph(G)) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_ego(self):
        G=nx.star_graph(3)
        H=nx.ego_graph(G,0)
        assert_true(nx.is_isomorphic(G,H))
        G.add_edge(1,11)
        G.add_edge(2,22)
        G.add_edge(3,33)
        H=nx.ego_graph(G,0)
        assert_true(nx.is_isomorphic(nx.star_graph(3),H))
        G=nx.path_graph(3)
        H=nx.ego_graph(G,0)
        assert_equal(H.edges(), [(0, 1)])
        H=nx.ego_graph(G,0,undirected=True)
        assert_equal(H.edges(), [(0, 1)])
        H=nx.ego_graph(G,0,center=False)
        assert_equal(H.edges(), []) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_neighbors_complete_graph(self):
        graph = nx.complete_graph(100)
        pop = random.sample(list(graph), 1)
        nbors = list(nx.neighbors(graph, pop[0]))
        # should be all the other vertices in the graph
        assert_equal(len(nbors), len(graph) - 1)

        graph = nx.path_graph(100)
        node = random.sample(list(graph), 1)[0]
        nbors = list(nx.neighbors(graph, node))
        # should be all the other vertices in the graph
        if node != 0 and node != 99:
            assert_equal(len(nbors), 2)
        else:
            assert_equal(len(nbors), 1)

        # create a star graph with 99 outer nodes
        graph = nx.star_graph(99)
        nbors = list(nx.neighbors(graph, 0))
        assert_equal(len(nbors), 99) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_non_edges(self):
        # All possible edges exist
        graph = nx.complete_graph(5)
        nedges = list(nx.non_edges(graph))
        assert_equal(len(nedges), 0)

        graph = nx.path_graph(4)
        expected = [(0, 2), (0, 3), (1, 3)]
        nedges = list(nx.non_edges(graph))
        for (u, v) in expected:
            assert_true((u, v) in nedges or (v, u) in nedges)

        graph = nx.star_graph(4)
        expected = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
        nedges = list(nx.non_edges(graph))
        for (u, v) in expected:
            assert_true((u, v) in nedges or (v, u) in nedges)

        # Directed graphs
        graph = nx.DiGraph()
        graph.add_edges_from([(0, 2), (2, 0), (2, 1)])
        expected = [(0, 1), (1, 0), (1, 2)]
        nedges = list(nx.non_edges(graph))
        for e in expected:
            assert_true(e in nedges) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_ego(self):
        G = nx.star_graph(3)
        H = nx.ego_graph(G, 0)
        assert_true(nx.is_isomorphic(G, H))
        G.add_edge(1, 11)
        G.add_edge(2, 22)
        G.add_edge(3, 33)
        H = nx.ego_graph(G, 0)
        assert_true(nx.is_isomorphic(nx.star_graph(3), H))
        G = nx.path_graph(3)
        H = nx.ego_graph(G, 0)
        assert_edges_equal(H.edges(), [(0, 1)])
        H = nx.ego_graph(G, 0, undirected=True)
        assert_edges_equal(H.edges(), [(0, 1)])
        H = nx.ego_graph(G, 0, center=False)
        assert_edges_equal(H.edges(), []) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_neighbors(self):
        graph = nx.complete_graph(100)
        pop = random.sample(list(graph), 1)
        nbors = list(nx.neighbors(graph, pop[0]))
        # should be all the other vertices in the graph
        assert_equal(len(nbors), len(graph) - 1)

        graph = nx.path_graph(100)
        node = random.sample(list(graph), 1)[0]
        nbors = list(nx.neighbors(graph, node))
        # should be all the other vertices in the graph
        if node != 0 and node != 99:
            assert_equal(len(nbors), 2)
        else:
            assert_equal(len(nbors), 1)

        # create a star graph with 99 outer nodes
        graph = nx.star_graph(99)
        nbors = list(nx.neighbors(graph, 0))
        assert_equal(len(nbors), 99) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_threshold_sequence_graph_test(self):
        G = nx.star_graph(10)
        assert_true(nxt.is_threshold_graph(G))
        assert_true(nxt.is_threshold_sequence(list(d for n, d in G.degree())))

        G = nx.complete_graph(10)
        assert_true(nxt.is_threshold_graph(G))
        assert_true(nxt.is_threshold_sequence(list(d for n, d in G.degree())))

        deg = [3, 2, 2, 1, 1, 1]
        assert_false(nxt.is_threshold_sequence(deg))

        deg = [3, 2, 2, 1]
        assert_true(nxt.is_threshold_sequence(deg))

        G = nx.generators.havel_hakimi_graph(deg)
        assert_true(nxt.is_threshold_graph(G)) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def _gen_star(self, n):
        for _ in range(n):
            num_v = np.random.randint(self.min_num_v, self.max_num_v)
            # nx.star_graph(N) gives a star graph with N+1 nodes
            g = nx.star_graph(num_v - 1)
            self.graphs.append(g)
            self.labels.append(1) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_no_backtracking():
    N = 5
    G = dgl.DGLGraph(nx.star_graph(N))
    L = G.line_graph(backtracking=False)
    assert L.number_of_nodes() == 2 * N
    for i in range(1, N):
        e1 = G.edge_id(0, i)
        e2 = G.edge_id(i, 0)
        assert not L.has_edge_between(e1, e2)
        assert not L.has_edge_between(e2, e1)

# reverse graph related 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_graph_directed():
    """
    Ensure we keep newly constructed topologies as directed graph
    """
    topo = complete_topology(5)
    assert isinstance(topo.get_graph(), networkx.DiGraph)
    # even if original graph is undirected
    topo = Topology(u'noname', networkx.star_graph(8))
    assert topo.get_graph().is_directed() 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_non_neighbors(self):
        graph = nx.complete_graph(100)
        pop = random.sample(graph.nodes(), 1)
        nbors = list(nx.non_neighbors(graph, pop[0]))
        # should be all the other vertices in the graph
        assert_equal(len(nbors), 0)

        graph = nx.path_graph(100)
        node = random.sample(graph.nodes(), 1)[0]
        nbors = list(nx.non_neighbors(graph, node))
        # should be all the other vertices in the graph
        if node != 0 and node != 99:
            assert_equal(len(nbors), 97)
        else:
            assert_equal(len(nbors), 98)

        # create a star graph with 99 outer nodes
        graph = nx.star_graph(99)
        nbors = list(nx.non_neighbors(graph, 0))
        assert_equal(len(nbors), 0)

        # disconnected graph
        graph = nx.Graph()
        graph.add_nodes_from(range(10))
        nbors = list(nx.non_neighbors(graph, 0))
        assert_equal(len(nbors), 9) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import star_graph [as 別名]
def test_S4(self):
        G = nx.star_graph(4)
        self.test(G, 1, 2, [0])