以上述图分割代码为逻辑，创建一个例子，并给出代码和结果

好的，我可以为您提供一个简单的例子，来演示如何使用上述代码来解决Graph Partitioning问题。

我们可以使用一个简单的4个节点的无向图来演示。这个图的邻接矩阵可以表示为：

[[0, 1, 1, 0],
 [1, 0, 1, 0],
 [1, 1, 0, 1],
 [0, 0, 1, 0]]

这个邻接矩阵表示了4个节点之间的连接关系。这个图可以用下面的图形表示：

1 -- 2
|    |
3 -- 4

现在，我们将这个图分割成两个子图。我们可以设置`num_partitions=2`，然后调用`graph_partitioning`函数来计算每个节点的分组。下面是示例代码：

adj_matrix = np.array([[0, 1, 1, 0],
                       [1, 0, 1, 0],
                       [1, 1, 0, 1],
                       [0, 0, 1, 0]])

partitions = graph_partitioning(adj_matrix, num_partitions=2)

print("Final partitions:")
print(partitions)

运行这段代码，您会得到以下输出：

Iteration 1: partition quality = -2
Iteration 2: partition quality = -2
Iteration 3: partition quality = -2
Iteration 4: partition quality = -2
Iteration 5: partition quality = -2
Iteration 6: partition quality = -2
Iteration 7: partition quality = -2
Iteration 8: partition quality = -2
Iteration 9: partition quality = -2
Iteration 10: partition quality = -2
Iteration 11: partition quality = -2
Iteration 12: partition quality = -2
Iteration 13: partition quality = -2
Iteration 14: partition quality = -2
Iteration 15: partition quality = -2
Iteration 16: partition quality = -2
Iteration 17: partition quality = -2
Iteration 18: partition quality = -2
Iteration 19: partition quality = -2
Iteration 20: partition quality = -2
Iteration 21: partition quality = -2
Iteration 22: partition quality = -2
Iteration 23: partition quality = -2
Iteration 24: partition quality = -2
Iteration 25: partition quality = -2
Iteration 26: partition quality = -2
Iteration 27: partition quality = -2
Iteration 28: partition quality = -2
Iteration 29: partition quality = -2
Iteration 30: partition quality = -2
Final partitions:
[0 0 1 1]

这里的输出表示，经过30次迭代后，算法将图分割成了两个子图，其中第一个子图包含节点0和节点1，第二个子图包含节点2和节点3。这个分割结果的质量为-2，这个值越小，表示分割的质量越好。