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src/graph_graphical.py

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+import networkx as nx
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+
+def random_walk(graph, start_node, walk_length):
+    walk = [start_node]
+    for _ in range(walk_length - 1):
+        neighbors = list(graph.neighbors(walk[-1]))
+        if not neighbors:
+            break
+        walk.append(np.random.choice(neighbors))
+    return walk
+
+def random_walks_matrix(graph, num_walks, walk_length):
+    nodes = list(graph.nodes)
+    visit_counts = {node: {neighbor: 0 for neighbor in nodes} for node in nodes}
+    
+    for node in nodes:
+        for _ in range(num_walks):
+            walk = random_walk(graph, node, walk_length)
+            for neighbor in walk:
+                visit_counts[node][neighbor] += 1
+    
+    visit_matrix = np.array([[visit_counts[node][neighbor] for neighbor in nodes] for node in nodes])
+    return visit_matrix
+
+def cluster_nodes(visit_matrix, num_clusters):
+    kmeans = KMeans(n_clusters=num_clusters, random_state=0)
+    kmeans.fit(visit_matrix)
+    return kmeans.labels_
+
+def critical_nodes(graph, visit_matrix, num_clusters):
+    initial_clusters = cluster_nodes(visit_matrix, num_clusters)
+    critical_nodes = []
+    
+    for node in graph.nodes:
+        temp_graph = graph.copy()
+        temp_graph.remove_node(node)
+        
+        temp_visit_matrix = random_walks_matrix(temp_graph, num_walks, walk_length)
+        new_clusters = cluster_nodes(temp_visit_matrix, num_clusters)
+        
+        if not np.array_equal(initial_clusters, new_clusters):
+            critical_nodes.append(node)
+    
+    return critical_nodes
+
+def draw_graph_with_clusters(graph, clusters, critical_nodes):
+    pos = nx.spring_layout(graph)
+    plt.figure(figsize=(10, 7))
+    
+    unique_clusters = np.unique(clusters)
+    colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_clusters)))
+    
+    for cluster, color in zip(unique_clusters, colors):
+        nodes_in_cluster = [node for node, cluster_id in enumerate(clusters) if cluster_id == cluster]
+        nx.draw_networkx_nodes(graph, pos, nodelist=nodes_in_cluster, node_color=[color], node_size=300)
+    
+    nx.draw_networkx_edges(graph, pos, alpha=0.5)
+    
+    critical_pos = {node: pos[node] for node in critical_nodes}
+    nx.draw_networkx_nodes(graph, pos, nodelist=critical_nodes, node_color='black', node_size=100, label='Critical Nodes')
+    
+    labels = {node: node for node in graph.nodes}
+    nx.draw_networkx_labels(graph, pos, labels, font_size=8)
+    
+    plt.title("Graph Clustering and Critical Nodes")
+    plt.legend()
+    plt.show()
+
+# Przykładowa użycie
+num_walks = 100
+walk_length = 10
+num_clusters = 3
+
+# Dla grafu nieskierowanego
+G = nx.karate_club_graph()
+visit_matrix = random_walks_matrix(G, num_walks, walk_length)
+clusters = cluster_nodes(visit_matrix, num_clusters)
+critical = critical_nodes(G, visit_matrix, num_clusters)
+
+print("Klasteryzacja:", clusters)
+print("Krytyczne wierzchołki:", critical)
+
+draw_graph_with_clusters(G, clusters, critical)