src/Algorithms/graph_louvain_pagerank.py

"""
MODULE ALGORITHM - AI METRICS (LOUVAIN & PAGERANK)
==================================================
Calcule et injecte les métriques directement dans les attributs des nœuds.
"""

import community.community_louvain as community_louvain
import networkx as nx

def apply_ai_algorithms(G: nx.Graph):
    ai_metadata = {}
    
    # 1. PAGERANK (Influence)
    try:
        pagerank_scores = nx.pagerank(G, alpha=0.85)
        nx.set_node_attributes(G, pagerank_scores, 'pagerank_score')
        
        # On ajoute le score dans le 'title' pour le survol souris
        for n, score in pagerank_scores.items():
            current_title = G.nodes[n].get('title', '')
            G.nodes[n]['title'] = f"{current_title}\nInfluence (PR): {score:.4f}".strip()
            
    except Exception as e:
        print(f"Erreur PageRank: {e}")

    # 2. LOUVAIN (Communautés)
    try:
        # Louvain nécessite un graphe non-dirigé
        G_undirected = G.to_undirected()
        partition = community_louvain.best_partition(G_undirected)
        
        nx.set_node_attributes(G, partition, 'community_id')
        
        # Stats pour l'IA
        num_communities = len(set(partition.values()))
        ai_metadata["louvain_communities_count"] = num_communities
        
        # Ajout info au survol
        for n, cid in partition.items():
            current_title = G.nodes[n].get('title', '')
            G.nodes[n]['title'] = f"{current_title}\nGroupe: #{cid}".strip()
            
    except Exception as e:
        print(f"Erreur Louvain: {e}")
        ai_metadata["louvain_communities_count"] = 0

    return G, ai_metadata