{"id":948567,"date":"2024-12-27T00:04:46","date_gmt":"2024-12-26T16:04:46","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/948567.html"},"modified":"2024-12-27T00:04:48","modified_gmt":"2024-12-26T16:04:48","slug":"python-networkx%e5%a6%82%e4%bd%95%e7%94%bb%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/948567.html","title":{"rendered":"python networkx\u5982\u4f55\u753b\u56fe"},"content":{"rendered":"

\"python<\/p>\n

Python\u4e2d\u7684NetworkX\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u6765\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u7814\u7a76\u590d\u6742\u7f51\u7edc\u3002\u8981\u4f7f\u7528NetworkX\u753b\u56fe\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5e93\uff0c\u7136\u540e\u521b\u5efa\u56fe\u5bf9\u8c61\u3001\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\uff0c\u6700\u540e\u4f7f\u7528Matplotlib\u5e93\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u5b89\u88c5NetworkX\u5e93\u3001\u521b\u5efa\u56fe\u5bf9\u8c61\u3001\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\u3001\u4f7f\u7528Matplotlib\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/strong> <\/p>\n<\/p>\n

NetworkX\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u7814\u7a76\u56fe\u7ed3\u6784\u7684Python\u5e93\u3002\u4e3a\u4e86\u5728Python\u4e2d\u4f7f\u7528NetworkX\u8fdb\u884c\u7ed8\u56fe\uff0c\u6211\u4eec\u9700\u8981\u4f9d\u8d56\u53e6\u4e00\u4e2a\u5e93Matplotlib\u6765\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5b89\u88c5\u4e86NetworkX\u548cMatplotlib\uff0c\u53ef\u4ee5\u901a\u8fc7pip install networkx matplotlib<\/code>\u6765\u5b8c\u6210\u3002\u521b\u5efa\u4e00\u4e2a\u56fe\u5bf9\u8c61\u662f\u7b2c\u4e00\u6b65\uff0cNetworkX\u652f\u6301\u591a\u79cd\u56fe\u7ed3\u6784\uff0c\u5305\u62ec\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u3001\u591a\u56fe\u7b49\u3002\u63a5\u4e0b\u6765\uff0c\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\u662f\u6784\u5efa\u56fe\u7684\u6838\u5fc3\u6b65\u9aa4\u3002\u6700\u540e\uff0c\u4f7f\u7528Matplotlib\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u8c03\u7528NetworkX\u7684\u7ed8\u56fe\u51fd\u6570\u5c06\u56fe\u5f62\u5316\u5c55\u793a\u51fa\u6765\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5NetworkX\u548cMatplotlib<\/p>\n<\/p>\n

\u5728\u4f7f\u7528NetworkX\u8fdb\u884c\u7ed8\u56fe\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5b89\u88c5\u4e86NetworkX\u548cMatplotlib\u5e93\u3002\u8fd9\u4e24\u4e2a\u5e93\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u8fdb\u884c\u5b89\u88c5\u3002\u5b89\u88c5\u547d\u4ee4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

pip install networkx matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u5bfc\u5165\u8fd9\u4e24\u4e2a\u5e93\uff0c\u4ee5\u4fbf\u4f7f\u7528\u5b83\u4eec\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u521b\u5efa\u56fe\u5bf9\u8c61<\/p>\n<\/p>\n
NetworkX\u63d0\u4f9b\u4e86\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u5bf9\u8c61\uff0c\u6700\u5e38\u89c1\u7684\u5305\u62ec\u65e0\u5411\u56fe\uff08Graph\uff09\u3001\u6709\u5411\u56fe\uff08DiGraph\uff09\u548c\u591a\u91cd\u56fe\uff08MultiGraph\uff09\u3002\u6bcf\u79cd\u56fe\u5bf9\u8c61\u90fd\u6709\u5176\u72ec\u7279\u7684\u7528\u9014\uff0c\u9009\u62e9\u5408\u9002\u7684\u56fe\u7c7b\u578b\u662f\u6784\u5efa\u7f51\u7edc\u7684\u7b2c\u4e00\u6b65\u3002<\/p>\n<\/p>\n
    \n
  1. \n
    \u65e0\u5411\u56fe<\/strong>\uff1a<\/p>\n
    \u65e0\u5411\u56fe\u662f\u6700\u7b80\u5355\u7684\u56fe\u7ed3\u6784\uff0c\u5176\u4e2d\u7684\u8fb9\u6ca1\u6709\u65b9\u5411\u6027\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a\u65e0\u5411\u56fe\uff1a<\/p>\n<\/p>\n
    import networkx as nx<\/p>\n
    G = nx.Graph()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u6709\u5411\u56fe<\/strong>\uff1a<\/p>\n
    \u6709\u5411\u56fe\u4e2d\u7684\u8fb9\u662f\u6709\u65b9\u5411\u7684\uff0c\u9002\u7528\u4e8e\u8868\u793a\u65b9\u5411\u6027\u5173\u7cfb\u7684\u7f51\u7edc\u3002\u521b\u5efa\u6709\u5411\u56fe\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    D = nx.DiGraph()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  3. \n
    \u591a\u91cd\u56fe<\/strong>\uff1a<\/p>\n
    \u591a\u91cd\u56fe\u5141\u8bb8\u4e24\u4e2a\u8282\u70b9\u4e4b\u95f4\u6709\u591a\u6761\u8fb9\uff0c\u9002\u5408\u8868\u793a\u590d\u6742\u7684\u5173\u7cfb\u7f51\u7edc\u3002\u521b\u5efa\u591a\u91cd\u56fe\u7684\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    M = nx.MultiGraph()<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e09\u3001\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9<\/p>\n<\/p>\n
    \u4e00\u65e6\u521b\u5efa\u4e86\u56fe\u5bf9\u8c61\uff0c\u4e0b\u4e00\u6b65\u5c31\u662f\u5411\u56fe\u4e2d\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\u3002\u8282\u70b9\u53ef\u4ee5\u662f\u4efb\u4f55\u53ef\u54c8\u5e0c\u7684\u5bf9\u8c61\uff0c\u800c\u8fb9\u5219\u662f\u5728\u4e24\u4e2a\u8282\u70b9\u4e4b\u95f4\u5efa\u7acb\u7684\u8fde\u63a5\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u6dfb\u52a0\u8282\u70b9<\/strong>\uff1a<\/p>\n<\/p>\n
      \u53ef\u4ee5\u4f7f\u7528add_node()<\/code>\u65b9\u6cd5\u9010\u4e2a\u6dfb\u52a0\u8282\u70b9\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528add_nodes_from()<\/code>\u65b9\u6cd5\u6279\u91cf\u6dfb\u52a0\u8282\u70b9\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
      G.add_node(1)<\/p>\n
      G.add_nodes_from([2, 3, 4])<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u6dfb\u52a0\u8fb9<\/strong>\uff1a<\/p>\n<\/p>\n
      \u4f7f\u7528add_edge()<\/code>\u65b9\u6cd5\u6dfb\u52a0\u5355\u6761\u8fb9\uff0c\u6216add_edges_from()<\/code>\u65b9\u6cd5\u6279\u91cf\u6dfb\u52a0\u591a\u6761\u8fb9\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
      G.add_edge(1, 2)<\/p>\n
      G.add_edges_from([(2, 3), (3, 4)])<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u56db\u3001\u4f7f\u7528Matplotlib\u8fdb\u884c\u53ef\u89c6\u5316<\/p>\n<\/p>\n
      NetworkX\u63d0\u4f9b\u4e86\u591a\u79cd\u7ed8\u56fe\u51fd\u6570\uff0c\u7ed3\u5408Matplotlib\u53ef\u4ee5\u5c06\u56fe\u5f62\u53ef\u89c6\u5316\u5c55\u793a\u3002\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u51fd\u6570\u662fdraw()<\/code>\uff0c\u5b83\u53ef\u4ee5\u7ed8\u5236\u57fa\u672c\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u7b80\u5355\u7ed8\u56fe<\/strong>\uff1a<\/p>\n<\/p>\n
        \u4f7f\u7528nx.draw()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u57fa\u672c\u7684\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        nx.draw(G, with_labels=True)<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u53c2\u6570with_labels=True<\/code>\u7528\u4e8e\u5728\u8282\u70b9\u4e0a\u663e\u793a\u6807\u7b7e\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u81ea\u5b9a\u4e49\u7ed8\u56fe<\/strong>\uff1a<\/p>\n<\/p>\n
        NetworkX\u7684\u7ed8\u56fe\u529f\u80fd\u652f\u6301\u591a\u79cd\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u4f8b\u5982\u8282\u70b9\u989c\u8272\u3001\u5927\u5c0f\u3001\u8fb9\u7684\u5bbd\u5ea6\u548c\u989c\u8272\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u81ea\u5b9a\u4e49\u53c2\u6570\uff1a<\/p>\n<\/p>\n
        nx.draw(G, with_labels=True, node_size=700, node_color='skyblue', edge_color='gray')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        • node_size<\/code>\uff1a\u8bbe\u7f6e\u8282\u70b9\u7684\u5927\u5c0f\u3002<\/li>\n
        • node_color<\/code>\uff1a\u8bbe\u7f6e\u8282\u70b9\u7684\u989c\u8272\u3002<\/li>\n
        • edge_color<\/code>\uff1a\u8bbe\u7f6e\u8fb9\u7684\u989c\u8272\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u5e03\u5c40\u7b97\u6cd5<\/p>\n<\/p>\n
          NetworkX\u63d0\u4f9b\u4e86\u591a\u79cd\u5e03\u5c40\u7b97\u6cd5\uff0c\u7528\u4e8e\u786e\u5b9a\u8282\u70b9\u5728\u56fe\u4e2d\u7684\u4f4d\u7f6e\u3002\u8fd9\u4e9b\u5e03\u5c40\u7b97\u6cd5\u6839\u636e\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u9009\u62e9\uff0c\u5e38\u7528\u7684\u5e03\u5c40\u5305\u62ec\uff1a<\/p>\n<\/p>\n
            \n
          1. \n
            Spring Layout<\/strong>\uff1a<\/p>\n
            \u8fd9\u662fNetworkX\u9ed8\u8ba4\u7684\u5e03\u5c40\u7b97\u6cd5\uff0c\u6a21\u62df\u5f39\u7c27\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u56fe\u5f62\u3002<\/p>\n<\/p>\n
            pos = nx.spring_layout(G)<\/p>\n
            nx.draw(G, pos, with_labels=True)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            Circular Layout<\/strong>\uff1a<\/p>\n
            \u8282\u70b9\u5728\u5706\u5468\u4e0a\u5747\u5300\u5206\u5e03\uff0c\u9002\u5408\u4e8e\u8282\u70b9\u6570\u76ee\u8f83\u5c11\u4e14\u5e0c\u671b\u7ed3\u6784\u5bf9\u79f0\u7684\u56fe\u3002<\/p>\n<\/p>\n
            pos = nx.circular_layout(G)<\/p>\n
            nx.draw(G, pos, with_labels=True)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          3. \n
            Random Layout<\/strong>\uff1a<\/p>\n
            \u8282\u70b9\u7684\u4f4d\u7f6e\u662f\u968f\u673a\u5206\u914d\u7684\uff0c\u9002\u7528\u4e8e\u5feb\u901f\u7ed8\u5236\u9884\u89c8\u3002<\/p>\n<\/p>\n
            pos = nx.random_layout(G)<\/p>\n
            nx.draw(G, pos, with_labels=True)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001\u4f7f\u7528\u56fe\u7684\u5c5e\u6027<\/p>\n<\/p>\n
            NetworkX\u5141\u8bb8\u5728\u8282\u70b9\u548c\u8fb9\u4e0a\u9644\u52a0\u5c5e\u6027\uff0c\u4ee5\u4fbf\u5b58\u50a8\u548c\u4f7f\u7528\u989d\u5916\u7684\u4fe1\u606f\u3002\u8fd9\u4e9b\u5c5e\u6027\u53ef\u4ee5\u7528\u6765\u4e30\u5bcc\u56fe\u7684\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u8282\u70b9\u5c5e\u6027<\/strong>\uff1a<\/p>\n<\/p>\n
              \u53ef\u4ee5\u5728\u6dfb\u52a0\u8282\u70b9\u65f6\u9644\u52a0\u5c5e\u6027\uff0c\u4e5f\u53ef\u4ee5\u5728\u6dfb\u52a0\u540e\u8bbe\u7f6e\u5c5e\u6027\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
              G.add_node(1, color='red', size=10)<\/p>\n
              nx.set_node_attributes(G, {1: 'blue'}, 'color')<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u83b7\u53d6\u8282\u70b9\u5c5e\u6027\u53ef\u4ee5\u4f7f\u7528get_node_attributes()<\/code>\uff1a<\/p>\n<\/p>\n
              color = nx.get_node_attributes(G, 'color')<\/p>\n
              print(color)<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            2. \n
              \u8fb9\u7684\u5c5e\u6027<\/strong>\uff1a<\/p>\n<\/p>\n
              \u6dfb\u52a0\u8fb9\u65f6\u53ef\u4ee5\u9644\u52a0\u5c5e\u6027\uff0c\u4e5f\u53ef\u4ee5\u5728\u6dfb\u52a0\u540e\u8bbe\u7f6e\u5c5e\u6027\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
              G.add_edge(1, 2, weight=4.2)<\/p>\n
              nx.set_edge_attributes(G, {(1, 2): 5.1}, 'weight')<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u83b7\u53d6\u8fb9\u5c5e\u6027\u53ef\u4ee5\u4f7f\u7528get_edge_attributes()<\/code>\uff1a<\/p>\n<\/p>\n
              weight = nx.get_edge_attributes(G, 'weight')<\/p>\n
              print(weight)<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
              \u4e03\u3001\u5e94\u7528\u5b9e\u4f8b<\/p>\n<\/p>\n
              \u4e0b\u9762\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u5177\u4f53\u7684\u4f8b\u5b50\u6765\u6f14\u793a\u5982\u4f55\u4f7f\u7528NetworkX\u521b\u5efa\u548c\u7ed8\u5236\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
              import networkx as nx<\/p>\n
              import matplotlib.pyplot as plt<\/p>\n
              \u521b\u5efa\u4e00\u4e2a\u6709\u5411\u56fe<\/strong><\/h2>\n
              G = nx.DiGraph()<\/p>\n
              \u6dfb\u52a0\u8282\u70b9<\/strong><\/h2>\n
              G.add_node(1, label='A')<\/p>\n
              G.add_node(2, label='B')<\/p>\n
              G.add_node(3, label='C')<\/p>\n
              \u6dfb\u52a0\u8fb9<\/strong><\/h2>\n
              G.add_edge(1, 2, weight=1.5)<\/p>\n
              G.add_edge(2, 3, weight=2.5)<\/p>\n
              G.add_edge(3, 1, weight=3.5)<\/p>\n
              \u8bbe\u7f6e\u5e03\u5c40<\/strong><\/h2>\n
              pos = nx.spring_layout(G)<\/p>\n
              \u7ed8\u5236\u8282\u70b9\u548c\u8fb9<\/strong><\/h2>\n
              nx.draw(G, pos, with_labels=True, node_size=1000, node_color='lightblue', edge_color='gray')<\/p>\n
              \u83b7\u53d6\u5e76\u663e\u793a\u8fb9\u7684\u6743\u91cd<\/strong><\/h2>\n
              edge_labels = nx.get_edge_attributes(G, 'weight')<\/p>\n
              nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)<\/p>\n
              \u663e\u793a\u56fe<\/strong><\/h2>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u7684NetworkX\u5e93\u521b\u5efa\u548c\u53ef\u89c6\u5316\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u548c\u7f51\u7edc\u3002\u901a\u8fc7\u8c03\u6574\u5e03\u5c40\u3001\u6dfb\u52a0\u5c5e\u6027\u4ee5\u53ca\u81ea\u5b9a\u4e49\u7ed8\u56fe\u53c2\u6570\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u5c55\u793a\u4e0d\u540c\u7684\u7f51\u7edc\u7ed3\u6784\u548c\u4fe1\u606f\u3002\u968f\u7740\u5bf9NetworkX\u5e93\u7684\u6df1\u5165\u5b66\u4e60\uff0c\u60a8\u5c06\u80fd\u591f\u5728\u590d\u6742\u7f51\u7edc\u5206\u6790\u4e2d\u5e94\u7528\u8fd9\u4e9b\u6280\u80fd\u3002<\/p>\n<\/p>\n
              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
               \u5982\u4f55\u4f7f\u7528Python\u7684NetworkX\u5e93\u7ed8\u5236\u56fe\u5f62\uff1f<\/strong>
              \u8981\u4f7f\u7528NetworkX\u7ed8\u5236\u56fe\u5f62\uff0c\u60a8\u9700\u8981\u9996\u5148\u5b89\u88c5NetworkX\u5e93\u4ee5\u53caMatplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7pip install networkx matplotlib<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528NetworkX\u521b\u5efa\u56fe\u5e76\u8c03\u7528Matplotlib\u7684\u7ed8\u56fe\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a  <\/p>\n
              import networkx as nx\nimport matplotlib.pyplot as plt\n\nG = nx.Graph()  # \u521b\u5efa\u4e00\u4e2a\u65e0\u5411\u56fe\nG.add_edge(1, 2)  # \u6dfb\u52a0\u8fb9\nG.add_edge(2, 3)\nG.add_edge(1, 3)\n\nnx.draw(G, with_labels=True)  # \u7ed8\u5236\u56fe\u5f62\u5e76\u663e\u793a\u6807\u7b7e\nplt.show()  # \u663e\u793a\u56fe\u5f62\n<\/code><\/pre>\n
              NetworkX\u652f\u6301\u54ea\u4e9b\u56fe\u5f62\u7c7b\u578b\uff1f<\/strong>
              NetworkX\u652f\u6301\u591a\u79cd\u56fe\u5f62\u7c7b\u578b\uff0c\u5305\u62ec\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u3001\u591a\u91cd\u56fe\u7b49\u3002\u7528\u6237\u53ef\u4ee5\u6839\u636e\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u6765\u5b9e\u73b0\u4e0d\u540c\u7684\u56fe\u7ed3\u6784\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u9700\u8981\u8003\u8651\u8fb9\u7684\u65b9\u5411\u7684\u56fe\u5f62\uff0c\u53ef\u4ee5\u4f7f\u7528\u6709\u5411\u56fe\uff1b\u800c\u5bf9\u4e8e\u9700\u8981\u5141\u8bb8\u591a\u6761\u8fb9\u8fde\u63a5\u540c\u4e00\u5bf9\u8282\u70b9\u7684\u60c5\u51b5\uff0c\u53ef\u4ee5\u9009\u62e9\u591a\u91cd\u56fe\u3002<\/p>\n
              \u5982\u4f55\u81ea\u5b9a\u4e49\u56fe\u5f62\u7684\u5916\u89c2\uff1f<\/strong>
              NetworkX\u4e0eMatplotlib\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u53c2\u6570\u81ea\u5b9a\u4e49\u56fe\u5f62\u7684\u5916\u89c2\u3002\u53ef\u4ee5\u8c03\u6574\u8282\u70b9\u7684\u989c\u8272\u3001\u5f62\u72b6\u3001\u5927\u5c0f\uff0c\u4ee5\u53ca\u8fb9\u7684\u989c\u8272\u548c\u5bbd\u5ea6\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u8fd9\u6837\u8bbe\u7f6e\uff1a  <\/p>\n
              nx.draw(G, with_labels=True, node_color='lightblue', node_size=700, edge_color='gray', width=2)\n<\/code><\/pre>\n
              \u8fd9\u6837\u7684\u8bbe\u7f6e\u53ef\u4ee5\u4f7f\u56fe\u5f62\u5728\u89c6\u89c9\u4e0a\u66f4\u52a0\u7f8e\u89c2\u548c\u6613\u4e8e\u7406\u89e3\u3002<\/p>\n
              \u5982\u4f55\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3a\u6587\u4ef6\uff1f<\/strong>
              \u5728\u4f7f\u7528NetworkX\u7ed8\u5236\u56fe\u5f62\u540e\uff0c\u53ef\u4ee5\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3a\u6587\u4ef6\u3002\u4f7f\u7528Matplotlib\u7684savefig<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5b8c\u6210\u8fd9\u4e00\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u6587\u4ef6\uff1a  <\/p>\n
              plt.savefig('graph.png')\n<\/code><\/pre>\n
              \u8fd9\u4f7f\u5f97\u60a8\u53ef\u4ee5\u65b9\u4fbf\u5730\u5171\u4eab\u6216\u5728\u5176\u4ed6\u6587\u6863\u4e2d\u4f7f\u7528\u751f\u6210\u7684\u56fe\u5f62\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u4e2d\u7684NetworkX\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u6765\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u7814\u7a76\u590d\u6742\u7f51\u7edc\u3002\u8981\u4f7f\u7528NetworkX\u753b […]","protected":false},"author":3,"featured_media":948573,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948567"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=948567"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948567\/revisions"}],"predecessor-version":[{"id":948574,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948567\/revisions\/948574"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/948573"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=948567"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=948567"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=948567"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}