我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用networkx.to_scipy_sparse_matrix()。
def learn_embedding(self, graph=None, edge_f=None, is_weighted=False, no_python=False): if not graph and not edge_f: raise Exception('graph/edge_f needed') if not graph: graph = graph_util.loadGraphFromEdgeListTxt(edge_f) graph = graph.to_undirected() t1 = time() A = nx.to_scipy_sparse_matrix(graph) normalize(A, norm='l1', axis=1, copy=False) I_n = sp.eye(graph.number_of_nodes()) I_min_A = I_n - A u, s, vt = lg.svds(I_min_A, k=self._d + 1, which='SM') t2 = time() self._X = vt.T self._X = self._X[:, 1:] return self._X, (t2 - t1)
def parse_cora_sparse(): path = "%s/../data/cora/" % (current_dir,) features, labels, id2index = _parse_cora_features_labels() n_papers = len(id2index) graph = nx.Graph() with open(path + 'cora.cites', 'r') as f: for line in f.xreadlines(): items = line.strip().split('\t') tail = id2index[items[0]] head = id2index[items[1]] graph.add_edge(head, tail) adj = nx.to_scipy_sparse_matrix(graph, format='csr') return adj.astype('float32'), features.astype('float32'), labels.astype('int32')
def _trans(g, deg): """ Given graph g, and inverse degree matrix. Returns transition matrix for g (uniform over adjacent vertices). """ adj = nx.to_scipy_sparse_matrix(g, format = "csc") return (deg * adj).T
def learn_embedding(self, graph=None, edge_f=None, is_weighted=False, no_python=False): if not graph and not edge_f: raise Exception('graph/edge_f needed') if not graph: graph = graph_util.loadGraphFromEdgeListTxt(edge_f) t1 = time() # A = nx.to_scipy_sparse_matrix(graph) # I = sp.eye(graph.number_of_nodes()) # M_g = I - self._beta*A # M_l = self._beta*A A = nx.to_numpy_matrix(graph) M_g = np.eye(graph.number_of_nodes()) - self._beta * A M_l = self._beta * A S = np.dot(np.linalg.inv(M_g), M_l) u, s, vt = lg.svds(S, k=self._d // 2) X1 = np.dot(u, np.diag(np.sqrt(s))) X2 = np.dot(vt.T, np.diag(np.sqrt(s))) t2 = time() self._X = np.concatenate((X1, X2), axis=1) p_d_p_t = np.dot(u, np.dot(np.diag(s), vt)) eig_err = np.linalg.norm(p_d_p_t - S) print('SVD error (low rank): %f' % eig_err) return self._X, (t2 - t1)
def directed_modularity_matrix(G, nodelist=None): """ INCLUDED FOR TESTING PURPOSES - Not implemented yet. Return the directed modularity matrix of G. The modularity matrix is the matrix B = A - <A>, where A is the adjacency matrix and <A> is the expected adjacency matrix, assuming that the graph is described by the configuration model. More specifically, the element B_ij of B is defined as B_ij = A_ij - k_i(out) k_j(in)/m where k_i(in) is the in degree of node i, and k_j(out) is the out degree of node j, with m the number of edges in the graph. Parameters ---------- G : DiGraph A NetworkX DiGraph nodelist : list, optional The rows and columns are ordered according to the nodes in nodelist. If nodelist is None, then the ordering is produced by G.nodes(). Returns ------- B : Numpy matrix The modularity matrix of G. Notes ----- NetworkX defines the element A_ij of the adjacency matrix as 1 if there is a link going from node i to node j. Leicht and Newman use the opposite definition. This explains the different expression for B_ij. See Also -------- to_numpy_matrix adjacency_matrix laplacian_matrix modularity_matrix References ---------- .. [1] E. A. Leicht, M. E. J. Newman, "Community structure in directed networks", Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008. """ if nodelist is None: nodelist = G.nodes() A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, format='csr') k_in = A.sum(axis=0) k_out = A.sum(axis=1) m = G.number_of_edges() # Expected adjacency matrix X = k_out * k_in / m return A - X
def aga_contract_graph(adata, min_group_size=0.01, max_n_contractions=1000, copy=False): """Contract the abstracted graph. """ adata = adata.copy() if copy else adata if 'aga_adjacency_tree_confidence' not in adata.uns: raise ValueError('run tool aga first!') min_group_size = min_group_size if min_group_size >= 1 else int(min_group_size * adata.n_smps) logg.info('contract graph using `min_group_size={}`'.format(min_group_size)) def propose_nodes_to_contract(adjacency_tree_confidence, node_groups): # nodes with two edges n_edges_per_seg = np.sum(adjacency_tree_confidence > 0, axis=1).A1 for i in range(adjacency_tree_confidence.shape[0]): if n_edges_per_seg[i] == 2: neighbors = adjacency_tree_confidence[i].nonzero()[1] for neighbors_edges in range(1, 20): for n_cnt, n in enumerate(neighbors): if n_edges_per_seg[n] == neighbors_edges: logg.msg('merging node {} into {} (two edges)' .format(i, n), v=4) return i, n # node groups with a very small cell number for i in range(adjacency_tree_confidence.shape[0]): if node_groups[str(i) == node_groups].size < min_group_size: neighbors = adjacency_tree_confidence[i].nonzero()[1] neighbor_sizes = [node_groups[str(n) == node_groups].size for n in neighbors] n = neighbors[np.argmax(neighbor_sizes)] logg.msg('merging node {} into {} ' '(smaller than `min_group_size` = {})' .format(i, n, min_group_size), v=4) return i, n return 0, 0 def contract_nodes(adjacency_tree_confidence, node_groups): for count in range(max_n_contractions): i, n = propose_nodes_to_contract(adjacency_tree_confidence, node_groups) if i != 0 or n != 0: G = nx.Graph(adjacency_tree_confidence) G_contracted = nx.contracted_nodes(G, n, i, self_loops=False) adjacency_tree_confidence = nx.to_scipy_sparse_matrix(G_contracted) node_groups[str(i) == node_groups] = str(n) for j in range(i+1, G.size()+1): node_groups[str(j) == node_groups] = str(j-1) else: break return adjacency_tree_confidence, node_groups size_before = adata.uns['aga_adjacency_tree_confidence'].shape[0] adata.uns['aga_adjacency_tree_confidence'], adata.smp['aga_groups'] = contract_nodes( adata.uns['aga_adjacency_tree_confidence'], adata.smp['aga_groups'].values) adata.uns['aga_groups_order'] = np.unique(adata.smp['aga_groups'].values) for key in ['aga_adjacency_full_confidence', 'aga_groups_original', 'aga_groups_order_original', 'aga_groups_colors_original']: if key in adata.uns: del adata.uns[key] logg.info(' contracted graph from {} to {} nodes' .format(size_before, adata.uns['aga_adjacency_tree_confidence'].shape[0])) logg.msg('removed adata.uns["aga_adjacency_full_confidence"]', v=4) return adata if copy else None
