the output of dgl cannot correspond to orginal node id

Question

I am running a program to construct a heterogeneous graph and then calculating the network indicator of the pointed node using NetworkX. However, in the output Excel file, the column for the pointed node(COLUME 's') contains duplicated values that do not match their original ID numbers.

import pandas as pd
import dgl
import torch as th
import torch.nn.functional as F
import torch.nn as nn
from dgl.nn import GraphConv
from sklearn.preprocessing import LabelEncoder
import numpy as np
import networkx as nx

# Load data from Excel file

# Create a LabelEncoder object
label_encoder = LabelEncoder()

# Extract data from different sheets
...

# Initialize result_df here
...
# Walk through the data and collect node ids before creating the graph
node_set = set()
for index, row in data.iterrows():
node_set.add(row['s'])
if 'sh' in row:
node_set.add(row['sh'])
if 'b' in row:
node_set.add(row['b'])
if 'd' in row:
node_set.add(row['d'])

num_nodes = len(node_set)

# Convert data to int32 type
...

# Create d-s,sh-s,b-s diagrams in turn
...

# Use coded node ids when creating heterogeneous graphs
def create_hetero_graph(e_data, l_data, c_data):

src_sh = e_data['sh_id'].values.astype(np.int64)
dst_scode_sh = e_data['s_id'].values.astype(np.int64)

src_b = l_data['b_id'].values.astype(np.int64)
dst_s_b = l_data['s_id'].values.astype(np.int64)

src_d = c_data['d_id'].values.astype(np.int64)
dst_sc_d = c_data['s_id'].values.astype(np.int64)

edges = {
('sh', 'sh_to_s', 's'): (src_sh, dst_s_sh),
('b', 'b_to_s', 's'): (src_b, dst_s_b),
('d', 'd_to_s', 's'): (src_d, dst_s_d),
}

num_nodes_dict = {
'sh': src_sh.max() + 1,
'b': src_b.max() + 1,
'd': src_d.max() + 1,
's': max(dst_sc_sh.max(), dst_s_b.max(), dst_s_d.max()) + 1
}


g = dgl.heterograph(edges, num_nodes_dict=num_nodes_dict)


scode_encoder = LabelEncoder()
scode_encoder.fit(e_data['s'].astype(str))


decoded_s_sh = s_encoder.inverse_transform(dst_s_sh)
decoded_s_b = s_encoder.inverse_transform(dst_s_b)
decoded_s_d = s_encoder.inverse_transform(dst_s_d)


return g

hetero_graph = create_hetero_graph(e_data, l_data, c_data)

nx_g = nx.Graph(hetero_graph.to_networkx().edges())

# Continue the network analysis operation

# Calculate the degree centrality of the node
...


# Adds the centrality indicator to the result data box
result_df = pd.concat([result_df, pd.DataFrame(s_centralities, index=[0])], ignore_index=True)

result_df.reset_index(drop=True, inplace=True)


unique_labels = label_encoder.classes_

# Replace unknown tags with the most common category
most_common_label = result_df['s_id'].mode()[0] # Gets the most common category
result_df['s_id'] = result_df['s_id'].apply(lambda x: x if x in unique_labels else most_common_label)

# Process the nan value in the resulting data box
result_df['s_id'] = result_df['s_id'].fillna(most_common_label) # Fill nan values with the most common categories

# Reverse conversion tag
result_df['s'] = label_encoder.inverse_transform(result_df['s_id'].astype(int))

# Number of nodes
num_nodes = len(nx_g.nodes())

# Build a Series object for node centrality indicators
degree_centrality_series = pd.Series([degree_centrality.get(node, 0) for node in range(num_nodes)])


# Adds the node centrality indicator to the result data box
result_df['degree_centrality'] = degree_centrality_series


# Save data frames as Excel files (including centrality metrics)
....

I have debugged the codes several times, but I am unable to obtain satisfactory output. I hope to receive inspiring ideas and gain further insights on the centrality of heterogeneous graphs.