feat: add minimum spanning tree tests

README.md

@@ -347,6 +347,7 @@ algorithm Prim(G, root):
 **Examples**
 
 - Maze problem: [java](java-algorithm/src/main/java/com/example/algorithm/graph) | A maze problem is that find a path from the start to the goal. The maze is represented by a graph. The start and the goal are represented by vertices. The path is represented by a sequence of vertices.
+- Minimum spanning tree (Kruskal, Prim, Boruvka), CCSP#4.4.2: [python](python-algorithm/algorithm/graph/test)(test) | Find the minimum spanning tree of a graph.
 
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python-algorithm/algorithm/graph/test/test_minimum_spanning_tree.py

@@ -0,0 +1,44 @@
+import matplotlib.pyplot as plt
+import networkx
+import pytest
+
+from algorithm.graph.test.graph_data_utils import create_weighted_city_graph
+
+city_graph = create_weighted_city_graph()
+
+
+@pytest.mark.skip(reason='This test is for visualization only')
+def test_graph_mst_temp():
+    mst = networkx.minimum_spanning_tree(city_graph, algorithm='kruskal')
+    pos = networkx.spring_layout(mst)
+    networkx.draw_networkx_nodes(mst, pos)
+    networkx.draw_networkx_edges(mst, pos, width=1)
+    networkx.draw_networkx_labels(mst, pos, font_size=10)
+    plt.show()
+
+
+city_edges = {('Seattle', 'San Francisco'), ('San Francisco', 'Los Angeles'), ('Los Angeles', 'Riverside'),
+              ('Riverside', 'Phoenix'), ('Phoenix', 'Dallas'), ('Dallas', 'Houston'), ('Houston', 'Atlanta'),
+              ('Atlanta', 'Miami'), ('Atlanta', 'Washington'), ('Washington', 'Philadelphia'),
+              ('Philadelphia', 'New York'), ('New York', 'Boston'), ('Washington', 'Detroit'), ('Detroit', 'Chicago')}
+
+
+@pytest.mark.benchmark(group='graph_minimum_spanning_tree')
+@pytest.mark.parametrize(
+    argnames='graph, algorithm, expected_total_weight, expected_edges',
+    argvalues=[
+        (city_graph, 'kruskal', 5372, city_edges),
+        (city_graph, 'prim', 5372, city_edges),
+        (city_graph, 'boruvka', 5372, city_edges),
+    ],
+    ids=['kruskal', 'prim', 'boruvka'])
+def test_graph_mst(benchmark, graph, algorithm, expected_total_weight, expected_edges):
+    mst = benchmark(networkx.minimum_spanning_tree, graph, algorithm=algorithm)
+
+    mst_total_weight = sum(d['weight'] for u, v, d in mst.edges(data=True))
+    assert expected_total_weight == mst_total_weight
+
+    mst_edges = set((u, v) for u, v, d in mst.edges(data=True))
+    normalized_set1 = {tuple(sorted(edge)) for edge in expected_edges}
+    normalized_set2 = {tuple(sorted(edge)) for edge in mst_edges}
+    assert normalized_set1 == normalized_set2