normalization.py

import numpy as np
import scipy.sparse as sp
import torch

def aug_normalized_adjacency(adj):
   adj = adj + sp.eye(adj.shape[0]) #  A + I，A
   adj = sp.coo_matrix(adj)
   row_sum = np.array(adj.sum(1))
   d_inv_sqrt = np.power(row_sum, -0.5).flatten()
   d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
   d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
   return d_mat_inv_sqrt.dot(adj).dot(d_mat_inv_sqrt).tocoo()

def FAME_normalized_adjacency(adj):
   adj = sp.coo_matrix(adj)
   row_sum = np.array(adj.sum(1))
   d_inv_sqrt = np.power(row_sum, -1).flatten()
   d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
   d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
   return d_mat_inv_sqrt.dot(adj).tocoo()

def fetch_normalization(type):
   switcher = {
       'AugNormAdj': aug_normalized_adjacency,  # A' = (D + I)^-1/2 * ( A + I ) * (D + I)^-1/2,
       'FAMENormAdj': FAME_normalized_adjacency,
   } # dict
   func = switcher.get(type, lambda: "Invalid normalization technique.")
   return func

def row_normalize(mx):
    """Row-normalize sparse matrix"""
    rowsum = np.array(mx.sum(1))
    r_inv = np.power(rowsum, -1).flatten()
    r_inv[np.isinf(r_inv)] = 0.
    r_mat_inv = sp.diags(r_inv)
    mx = r_mat_inv.dot(mx)
    return mx