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  1. import numpy as np
  2. import scipy.sparse as sp
  3. def sparse_to_tuple(sparse_mx):
  4. if not sp.isspmatrix_coo(sparse_mx):
  5. sparse_mx = sparse_mx.tocoo()
  6. coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
  7. values = sparse_mx.data
  8. shape = sparse_mx.shape
  9. return coords, values, shape
  10. def normalize_adjacency_matrix(adj):
  11. adj = sp.coo_matrix(adj)
  12. if adj.shape[0] == adj.shape[1]:
  13. adj_ = adj + sp.eye(adj.shape[0])
  14. rowsum = np.array(adj_.sum(1))
  15. degree_mat_inv_sqrt = np.power(rowsum, -0.5).flatten()
  16. degree_mat_inv_sqrt = sp.diags(degree_mat_inv_sqrt)
  17. adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt)
  18. else:
  19. rowsum = np.array(adj.sum(1))
  20. colsum = np.array(adj.sum(0))
  21. rowdegree_mat_inv = sp.diags(np.nan_to_num(np.power(rowsum, -0.5)).flatten())
  22. coldegree_mat_inv = sp.diags(np.nan_to_num(np.power(colsum, -0.5)).flatten())
  23. adj_normalized = rowdegree_mat_inv.dot(adj).dot(coldegree_mat_inv).tocoo()
  24. return sparse_to_tuple(adj_normalized)