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import json
import networkx as nx
import community as community_louvain # You may need to install this package using: pip install python-louvain
def main():
# Load the JSON file
with open('arxiv2023_1624-10.json', 'r', encoding='utf-8') as f:
data = json.load(f)
# Initialize counters and sets
nodes_count = len(data)
classes = set()
train_nodes_count = 0
validation_nodes_count = 0
test_nodes_count = 0
# Build an undirected graph
G = nx.Graph()
# Iterate over each element in the dataset
for entry in data:
node_id = entry['node_id']
label = entry['label']
mask = entry['mask']
# Add label to the classes set
classes.add(label)
# Add the node with its attributes to the graph
G.add_node(node_id, label=label, mask=mask)
# Count nodes by mask type
if mask == 'Train':
train_nodes_count += 1
elif mask == 'Validation':
validation_nodes_count += 1
elif mask == 'Test':
test_nodes_count += 1
# Process neighbors and add edges (using set to remove duplicates)
neighbors = set(entry['neighbors'])
for neighbor in neighbors:
# Avoid self-loop if desired (optional)
if neighbor != node_id:
G.add_edge(node_id, neighbor)
# If you want to add self-loops, remove the above condition
# Compute the number of edges in the graph
edge_count = G.number_of_edges()
classes_count = len(classes)
# Perform Louvain community detection
partition = community_louvain.best_partition(G)
communities = set(partition.values())
community_count = len(communities)
# Print out the statistics
print("Nodes count:", nodes_count)
print("Edges count:", edge_count)
print("Classes count:", classes_count)
print("Train nodes count:", train_nodes_count)
print("Validation nodes count:", validation_nodes_count)
print("Test nodes count:", test_nodes_count)
print("Louvain community count:", community_count)
if __name__ == '__main__':
main()
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