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()