{"id":175862,"date":"2024-05-08T18:59:32","date_gmt":"2024-05-08T10:59:32","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/175862.html"},"modified":"2024-05-08T18:59:37","modified_gmt":"2024-05-08T10:59:37","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%9a%84networkx%e5%ba%93%e7%bb%98%e5%88%b6%e5%87%ba%e5%a6%82%e4%b8%8b%e6%95%b0%e6%8d%ae%e7%9a%84%e7%a4%be%e4%bc%9a%e7%bd%91%e7%bb%9c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/175862.html","title":{"rendered":"\u5982\u4f55\u7528python\u7684networkx\u5e93\u7ed8\u5236\u51fa\u5982\u4e0b\u6570\u636e\u7684\u793e\u4f1a\u7f51\u7edc"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u7684networkx\u5e93\u7ed8\u5236\u51fa\u5982\u4e0b\u6570\u636e\u7684\u793e\u4f1a\u7f51\u7edc\"<\/p>\n

\u4f7f\u7528Python\u7684NetworkX\u5e93\u7ed8\u5236\u793e\u4f1a\u7f51\u7edc\u56fe\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u7406\u89e3\u548c\u5206\u6790\u7f51\u7edc\u4e2d\u7684\u7ed3\u6784\u548c\u5173\u7cfb\u3002\u9996\u5148\uff0c\u786e\u4fddPython\u73af\u5883\u4e2d\u5df2\u5b89\u88c5NetworkX\u5e93\u4ee5\u53ca\u7528\u4e8e\u7ed8\u56fe\u7684matplotlib\u5e93\u3001\u7136\u540e\u5bfc\u5165\u8fd9\u4e9b\u5e93\u3001\u63a5\u7740\u521b\u5efa\u4e00\u4e2a\u56fe\u5bf9\u8c61\u3001\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\u3001\u6700\u540e\u5229\u7528matplotlib\u7ed8\u5236\u51fa\u7f51\u7edc\u56fe<\/strong>\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u4e0d\u4ec5\u6d89\u53ca\u5230\u57fa\u672c\u7684\u56fe\u5f62\u521b\u5efa\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u56fe\u8fdb\u884c\u7f8e\u5316\u548c\u5b9a\u5236\uff0c\u4fbf\u4e8e\u5206\u6790\u548c\u6f14\u793a\u3002<\/p>\n<\/p>\n

\u5176\u4e2d\uff0c\u786e\u4fddPython\u73af\u5883\u4e2d\u5df2\u5b89\u88c5NetworkX\u5e93\u4ee5\u53ca\u7528\u4e8e\u7ed8\u56fe\u7684matplotlib\u5e93<\/strong>\u662f\u9996\u8981\u6b65\u9aa4\uff0c\u56e0\u4e3a\u6240\u6709\u7684\u7ed8\u56fe\u64cd\u4f5c\u90fd\u9700\u8981\u8fd9\u4e24\u4e2a\u5e93\u7684\u652f\u6301\u3002NetworkX\u662f\u4e00\u4e2a\u7528Python\u8bed\u8a00\u5f00\u53d1\u7684\u56fe\u8bba\u4e0e\u590d\u6742\u7f51\u7edc\u5efa\u6a21\u5de5\u5177\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7f51\u7edc\u7ed3\u6784\u3001\u5206\u6790\u548c\u7ed8\u56fe\u5de5\u5177\uff0c\u800cmatplotlib\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684Python\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u5404\u79cd\u9759\u6001\u3001\u52a8\u6001\u3001\u4ea4\u4e92\u5f0f\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5NETWORKX\u4e0eMATPLOTLIB<\/h3>\n<\/p>\n

\u5b89\u88c5\u8fd9\u4e24\u4e2a\u5e93\u7684\u8fc7\u7a0b\u975e\u5e38\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u6765\u5b9e\u73b0\u3002\u6253\u5f00\u7ec8\u7aef\u6216\u547d\u4ee4\u63d0\u793a\u7b26\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n

pip install networkx matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u547d\u4ee4\u6765\u68c0\u9a8c\u8fd9\u4e24\u4e2a\u5e93\u662f\u5426\u5b89\u88c5\u6210\u529f\u3002\u6bd4\u5982\uff0c\u53ef\u4ee5\u5bfc\u5165networkx\u5e76\u67e5\u770b\u5176\u7248\u672c\u6765\u786e\u8ba4\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5bfc\u5165\u6240\u9700\u7684\u5e93<\/h3>\n<\/p>\n
\u5728Python\u811a\u672c\u7684\u5f00\u59cb\uff0c\u5bfc\u5165NetworkX\u548cmatplotlib\u7684pyplot\u3002\u901a\u5e38\u6211\u4eec\u4f1a\u7ed9matplotlib\u7684pyplot\u8bbe\u7f6e\u4e00\u4e2a\u522b\u540dplt\uff0c\u8fd9\u5df2\u6210\u4e3a\u4e00\u4e2a\u5e7f\u6cdb\u9075\u5faa\u7684\u7ea6\u5b9a\u3002<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u521b\u5efa\u56fe\u5bf9\u8c61<\/h3>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u521b\u5efa\u4e00\u4e2a\u56fe\u5bf9\u8c61\u6765\u5b58\u50a8\u793e\u4f1a\u7f51\u7edc\u7684\u7ed3\u6784\u3002NetworkX\u652f\u6301\u591a\u79cd\u7c7b\u578b\u7684\u56fe\uff0c\u5982\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u7b49\u3002\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u56fe\u7c7b\u578b\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u65e0\u5411\u56fe<\/p>\n
G = nx.Graph()<\/p>\n
\u82e5\u9700\u521b\u5efa\u6709\u5411\u56fe\uff0c\u5219\u4f7f\u7528DiGraph\u7c7b<\/strong><\/h2>\n
G = nx.DiGraph()<\/strong><\/h2>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6dfb\u52a0\u8282\u70b9\u4e0e\u8fb9<\/h3>\n<\/p>\n
\u5728\u521b\u5efa\u56fe\u5bf9\u8c61\u4e4b\u540e\uff0c\u9700\u8981\u5411\u56fe\u4e2d\u6dfb\u52a0\u8282\u70b9(node)\u548c\u8fb9(edge)\u3002\u5728\u793e\u4f1a\u7f51\u7edc\u4e2d\uff0c\u8282\u70b9\u901a\u5e38\u8868\u793a\u4e2a\u4f53\u6216\u8005\u7ec4\u7ec7\uff0c\u800c\u8fb9\u4ee3\u8868\u8282\u70b9\u4e4b\u95f4\u7684\u5173\u7cfb\u6216\u8fde\u63a5\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u8282\u70b9<\/p>\n
G.add_node("A")<\/p>\n
G.add_node("B")<\/p>\n
G.add_node("C")<\/p>\n
\u6279\u91cf\u6dfb\u52a0\u8282\u70b9<\/strong><\/h2>\n
G.add_nodes_from(["D", "E", "F"])<\/p>\n
\u6dfb\u52a0\u8fb9<\/strong><\/h2>\n
G.add_edge("A", "B")<\/p>\n
G.add_edge("B", "C")<\/p>\n
\u6279\u91cf\u6dfb\u52a0\u8fb9<\/strong><\/h2>\n
G.add_edges_from([("A", "D"), ("B", "D"), ("C", "E"), ("E", "F")])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5229\u7528MATPLOTLIB\u7ed8\u5236\u7f51\u7edc\u56fe<\/h3>\n<\/p>\n
\u52a0\u5165\u4e86\u8282\u70b9\u548c\u8fb9\u4e4b\u540e\uff0c\u63a5\u4e0b\u6765\u5229\u7528matplotlib\u6765\u7ed8\u5236\u793e\u4f1a\u7f51\u7edc\u56fe\u3002NetworkX\u63d0\u4f9b\u4e86draw<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u57fa\u7840\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u540c\u65f6\u4e5f\u53ef\u4ee5\u914d\u7f6e\u591a\u79cd\u7ed8\u56fe\u53c2\u6570\u6765\u5b9a\u5236\u56fe\u5f62\u7684\u5916\u89c2\u3002<\/p>\n<\/p>\n
# \u7ed8\u5236\u7f51\u7edc\u56fe\uff0c\u9ed8\u8ba4\u5e03\u5c40<\/p>\n
nx.draw(G, with_labels=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u7f51\u7edc\u56fe\u7684\u53ef\u8bfb\u6027\u548c\u7f8e\u89c2\u6027\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u5e03\u5c40\u7b97\u6cd5\u6765\u8c03\u6574\u8282\u70b9\u7684\u4f4d\u7f6e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528spring_layout<\/code>\u8ba9\u5f7c\u6b64\u4e4b\u95f4\u6709\u8fde\u63a5\u7684\u8282\u70b9\u66f4\u9760\u8fd1\uff0c\u4ece\u800c\u4f7f\u6574\u4e2a\u56fe\u770b\u8d77\u6765\u66f4\u52a0\u81ea\u7136\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528 spring_layout<\/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
\u516d\u3001\u7f8e\u5316\u548c\u5b9a\u5236\u7ed8\u56fe<\/h3>\n<\/p>\n
NetworkX\u548cmatplotlib\u7ed3\u5408\u8d77\u6765\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u7ed8\u56fe\u5b9a\u5236\u529f\u80fd\u3002\u53ef\u4ee5\u8c03\u6574\u8282\u70b9\u7684\u5927\u5c0f\u3001\u989c\u8272\u3001\u5f62\u72b6\uff0c\u8fb9\u7684\u98ce\u683c\u3001\u989c\u8272\u7b49\uff0c\u751a\u81f3\u6dfb\u52a0\u6807\u7b7e\u548c\u6807\u9898\u3002<\/p>\n<\/p>\n
# \u5b9a\u5236\u5316\u7ed8\u56fe\u793a\u4f8b<\/p>\n
nx.draw(G, pos, with_labels=True, node_color='skyblue', node_size=700, edge_color='gray')<\/p>\n
plt.title("\u793a\u4f8b\u793e\u4f1a\u7f51\u7edc\u56fe")<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u53ea\u662f\u6700\u57fa\u7840\u7684\u4e00\u4e2a\u4f8b\u5b50\uff0c\u5b9e\u9645\u4e0aNetworkX\u548cmatplotlib\u7684\u5f3a\u5927\u529f\u80fd\u652f\u6301\u7740\u66f4\u52a0\u590d\u6742\u548c\u591a\u6837\u5316\u7684\u7f51\u7edc\u56fe\u7ed8\u5236\u9700\u6c42\u3002\u4e86\u89e3\u548c\u638c\u63e1\u8fd9\u4e24\u4e2a\u5e93\u7684\u5404\u79cd\u529f\u80fd\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4ece\u591a\u4e2a\u89d2\u5ea6\u5206\u6790\u548c\u5c55\u793a\u793e\u4f1a\u7f51\u7edc\u7684\u7ed3\u6784\u4e0e\u7279\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
1. \u5982\u4f55\u4f7f\u7528Python\u7684networkx\u5e93\u521b\u5efa\u793e\u4f1a\u7f51\u7edc\u56fe\uff1f<\/strong><\/p>\n
\u7b54\uff1a\u8981\u4f7f\u7528Python\u4e2d\u7684networkx\u5e93\u521b\u5efa\u793e\u4f1a\u7f51\u7edc\u56fe\uff0c\u9996\u5148\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u7a7a\u7684\u56fe\u5bf9\u8c61\uff0c\u7136\u540e\u4f7f\u7528add_node()\u65b9\u6cd5\u6dfb\u52a0\u8282\u70b9\uff0c\u4f7f\u7528add_edge()\u65b9\u6cd5\u6dfb\u52a0\u8fb9\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528networkx\u5e93\u4e2d\u7684\u5404\u79cd\u65b9\u6cd5\u548c\u529f\u80fd\u6765\u8fdb\u4e00\u6b65\u5b9a\u5236\u56fe\u5f62\u7684\u5916\u89c2\u548c\u5e03\u5c40\u3002\u8fd8\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u5c06\u56fe\u5f62\u7ed8\u5236\u51fa\u6765\u3002<\/p>\n
2. \u5982\u4f55\u5728\u793e\u4f1a\u7f51\u7edc\u56fe\u4e2d\u8868\u793a\u4e0d\u540c\u7c7b\u578b\u7684\u5173\u7cfb\uff1f<\/strong><\/p>\n
\u7b54\uff1a\u5728\u793e\u4f1a\u7f51\u7edc\u56fe\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u6216\u5f62\u72b6\u6765\u8868\u793a\u4e0d\u540c\u7c7b\u578b\u7684\u5173\u7cfb\u3002\u4f8b\u5982\uff0c\u5982\u679c\u6709\u4e00\u4e2a\u8868\u793a\u5bb6\u5ead\u5173\u7cfb\u7684\u8fb9\uff0c\u53ef\u4ee5\u4f7f\u7528\u7ea2\u8272\u8868\u793a\uff1b\u5982\u679c\u6709\u4e00\u4e2a\u8868\u793a\u53cb\u8c0a\u5173\u7cfb\u7684\u8fb9\uff0c\u53ef\u4ee5\u4f7f\u7528\u84dd\u8272\u8868\u793a\u3002\u60a8\u53ef\u4ee5\u5728\u7ed8\u5236\u56fe\u5f62\u65f6\u81ea\u5b9a\u4e49\u8282\u70b9\u548c\u8fb9\u7684\u5c5e\u6027\uff0c\u4f7f\u5176\u5177\u6709\u4e0d\u540c\u7684\u6837\u5f0f\u4e0e\u989c\u8272\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u5c55\u793a\u4e0d\u540c\u7c7b\u578b\u7684\u5173\u7cfb\u3002<\/p>\n
3. \u5982\u4f55\u4f7f\u7528networkx\u5e93\u7684\u5e03\u5c40\u529f\u80fd\u4f7f\u793e\u4f1a\u7f51\u7edc\u56fe\u66f4\u5177\u53ef\u8bfb\u6027\uff1f<\/strong><\/p>\n
\u7b54\uff1anetworkx\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u5e03\u5c40\u7b97\u6cd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u60a8\u5c06\u793e\u4f1a\u7f51\u7edc\u56fe\u5e03\u5c40\u5728\u53ef\u89c6\u5316\u754c\u9762\u4e0a\uff0c\u4ee5\u4f7f\u5176\u66f4\u5177\u53ef\u8bfb\u6027\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Spring\u5e03\u5c40\u53ef\u4ee5\u4f7f\u8282\u70b9\u6839\u636e\u5b83\u4eec\u4e4b\u95f4\u7684\u8fde\u901a\u6027\u8fdb\u884c\u6392\u5217\uff0c\u5e76\u4f7f\u5f97\u5173\u8054\u7d27\u5bc6\u7684\u8282\u70b9\u66f4\u9760\u8fd1\u5f7c\u6b64\u3002\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u5e03\u5c40\u7b97\u6cd5\uff0c\u5982Circular\u3001Random\u3001Kamada-KawAI<\/a>\u7b49\uff0c\u6839\u636e\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u5e03\u5c40\u7b97\u6cd5\uff0c\u4ee5\u786e\u4fdd\u793e\u4f1a\u7f51\u7edc\u56fe\u7684\u53ef\u8bfb\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u7684NetworkX\u5e93\u7ed8\u5236\u793e\u4f1a\u7f51\u7edc\u56fe\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u7406\u89e3\u548c\u5206\u6790\u7f51\u7edc\u4e2d\u7684\u7ed3\u6784\u548c\u5173\u7cfb\u3002\u9996\u5148\uff0c\u786e\u4fddP […]","protected":false},"author":3,"featured_media":175877,"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\/175862"}],"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=175862"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/175862\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/175877"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=175862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=175862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=175862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}