import networkx as nx<\/p>\nprint(nx.__version__)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5982\u679c\u6ca1\u6709\u62a5\u9519\uff0c\u5e76\u4e14\u8f93\u51fa\u4e86networkx\u7684\u7248\u672c\u53f7\uff0c\u8bf4\u660e\u5b89\u88c5\u6210\u529f\u3002\u63a5\u4e0b\u6765\uff0c\u4f60\u5c31\u53ef\u4ee5\u5f00\u59cb\u4f7f\u7528networkx\u8fdb\u884c\u56fe\u8bba\u76f8\u5173\u7684\u5f00\u53d1\u548c\u7814\u7a76\u4e86\u3002<\/p>\n<\/p>\n
\u4e94\u3001networkx\u7684\u57fa\u672c\u4f7f\u7528<\/h3>\n<\/p>\n
\u5b89\u88c5\u5b8cnetworkx\u540e\uff0c\u4f60\u53ef\u4ee5\u5f00\u59cb\u4f7f\u7528\u5b83\u6765\u521b\u5efa\u548c\u64cd\u4f5c\u56fe\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u57fa\u672c\u7684\u4f7f\u7528\u793a\u4f8b\uff0c\u5e2e\u52a9\u4f60\u5feb\u901f\u5165\u95e8\u3002<\/p>\n<\/p>\n
\u521b\u5efa\u56fe<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u5bfc\u5165networkx\u5e93\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2a\u56fe\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
import networkx as nx<\/p>\n\u521b\u5efa\u4e00\u4e2a\u7a7a\u56fe<\/strong><\/h2>\nG = nx.Graph()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528add_node()\u548cadd_edge()\u65b9\u6cd5\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9\uff1a<\/p>\n<\/p>\n
# \u6dfb\u52a0\u8282\u70b9<\/p>\nG.add_node(1)<\/p>\n
G.add_nodes_from([2, 3])<\/p>\n
\u6dfb\u52a0\u8fb9<\/strong><\/h2>\nG.add_edge(1, 2)<\/p>\n
G.add_edges_from([(2, 3), (3, 1)])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u67e5\u770b\u56fe\u7684\u4fe1\u606f<\/h4>\n<\/p>\n
networkx\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u67e5\u770b\u56fe\u7684\u4fe1\u606f\uff0c\u4f8b\u5982\u8282\u70b9\u5217\u8868\u3001\u8fb9\u5217\u8868\u3001\u5ea6\u3001\u90bb\u5c45\u7b49\uff1a<\/p>\n<\/p>\n
# \u67e5\u770b\u8282\u70b9<\/p>\nprint(G.nodes())<\/p>\n
\u67e5\u770b\u8fb9<\/strong><\/h2>\nprint(G.edges())<\/p>\n
\u67e5\u770b\u8282\u70b9\u7684\u5ea6<\/strong><\/h2>\nprint(G.degree(1))<\/p>\n
\u67e5\u770b\u8282\u70b9\u7684\u90bb\u5c45<\/strong><\/h2>\nprint(list(G.neighbors(1)))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u753b\u56fe<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528networkx\u548cmatplotlib\u5e93\u6765\u7ed8\u5236\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\nnx.draw(G, with_labels=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001networkx\u7684\u9ad8\u7ea7\u529f\u80fd<\/h3>\n<\/p>\n
\u9664\u4e86\u57fa\u672c\u64cd\u4f5c\uff0cnetworkx\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u56fe\u7684\u751f\u6210\u3001\u56fe\u7684\u64cd\u4f5c\u3001\u56fe\u7684\u7b97\u6cd5\u7b49\u3002<\/p>\n<\/p>\n
\u56fe\u7684\u751f\u6210<\/h4>\n<\/p>\n
networkx\u63d0\u4f9b\u4e86\u591a\u79cd\u56fe\u7684\u751f\u6210\u65b9\u6cd5\uff0c\u4f8b\u5982\u5b8c\u5168\u56fe\u3001\u8def\u5f84\u56fe\u3001\u5faa\u73af\u56fe\u7b49\uff1a<\/p>\n<\/p>\n
# \u751f\u6210\u4e00\u4e2a\u5b8c\u5168\u56fe<\/p>\nK = nx.complete_graph(5)<\/p>\n
\u751f\u6210\u4e00\u4e2a\u8def\u5f84\u56fe<\/strong><\/h2>\nP = nx.path_graph(5)<\/p>\n
\u751f\u6210\u4e00\u4e2a\u5faa\u73af\u56fe<\/strong><\/h2>\nC = nx.cycle_graph(5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56fe\u7684\u64cd\u4f5c<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u5bf9\u56fe\u8fdb\u884c\u5404\u79cd\u64cd\u4f5c\uff0c\u4f8b\u5982\u5b50\u56fe\u3001\u56fe\u7684\u5408\u5e76\u3001\u56fe\u7684\u5dee\u96c6\u7b49\uff1a<\/p>\n<\/p>\n
# \u83b7\u53d6\u5b50\u56fe<\/p>\nsubgraph = G.subgraph([1, 2])<\/p>\n
\u56fe\u7684\u5408\u5e76<\/strong><\/h2>\nH = nx.union(G, P)<\/p>\n
\u56fe\u7684\u5dee\u96c6<\/strong><\/h2>\nD = nx.difference(G, P)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56fe\u7684\u7b97\u6cd5<\/h4>\n<\/p>\n
networkx\u5b9e\u73b0\u4e86\u8bb8\u591a\u7ecf\u5178\u7684\u56fe\u7b97\u6cd5\uff0c\u4f8b\u5982\u6700\u77ed\u8def\u5f84\u3001\u6700\u5c0f\u751f\u6210\u6811\u3001\u6700\u5927\u6d41\u7b49\uff1a<\/p>\n<\/p>\n
# \u6700\u77ed\u8def\u5f84<\/p>\nshortest_path = nx.shortest_path(G, source=1, target=3)<\/p>\n
\u6700\u5c0f\u751f\u6210\u6811<\/strong><\/h2>\nmst = nx.minimum_spanning_tree(G)<\/p>\n
\u6700\u5927\u6d41<\/strong><\/h2>\nflow_value, flow_dict = nx.maximum_flow(G, 1, 3)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001networkx\u4e0e\u5176\u4ed6\u5e93\u7684\u96c6\u6210<\/h3>\n<\/p>\n
networkx\u53ef\u4ee5\u4e0e\u5176\u4ed6Python\u5e93\u96c6\u6210\uff0c\u8fdb\u4e00\u6b65\u6269\u5c55\u5176\u529f\u80fd\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4e0epandas\u5e93\u7ed3\u5408\u4f7f\u7528\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u4e0ematplotlib\u5e93\u7ed3\u5408\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\u4e0epandas\u7684\u96c6\u6210<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u5c06\u56fe\u7684\u6570\u636e\u8f6c\u6362\u4e3apandas\u7684DataFrame\u8fdb\u884c\u5206\u6790\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n\u5c06\u56fe\u7684\u8fb9\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\nedges_df = nx.to_pandas_edgelist(G)<\/p>\n
\u5c06DataFrame\u8f6c\u6362\u4e3a\u56fe<\/strong><\/h2>\nG_new = nx.from_pandas_edgelist(edges_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0ematplotlib\u7684\u96c6\u6210<\/h4>\n<\/p>\n
\u867d\u7136networkx\u81ea\u5e26\u7ed8\u56fe\u529f\u80fd\uff0c\u4f46\u4e0ematplotlib\u7ed3\u5408\u53ef\u4ee5\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u56fe\u5f62\u53ef\u89c6\u5316\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u4f7f\u7528matplotlib\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\npos = nx.spring_layout(G)<\/p>\n
nx.draw(G, pos, with_labels=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001networkx\u7684\u6269\u5c55\u548c\u63d2\u4ef6<\/h3>\n<\/p>\n
networkx\u6709\u8bb8\u591a\u6269\u5c55\u548c\u63d2\u4ef6\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u589e\u5f3a\u5176\u529f\u80fd\u3002\u4f8b\u5982\uff0cgraph-tool\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u56fe\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u56fe\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4f7f\u7528graph-tool<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528graph-tool\u5e93\u6765\u8fdb\u884c\u9ad8\u6027\u80fd\u7684\u56fe\u5904\u7406\uff1a<\/p>\n<\/p>\n
import graph_tool.all as gt<\/p>\n\u5c06networkx\u56fe\u8f6c\u6362\u4e3agraph-tool\u56fe<\/strong><\/h2>\nG_gt = gt.Graph(directed=False)<\/p>\n
G_gt.add_edge_list(G.edges())<\/p>\n
\u8fdb\u884c\u56fe\u7b97\u6cd5\u5904\u7406<\/strong><\/h2>\nshortest_path = gt.shortest_path(G_gt, source=G_gt.vertex(1), target=G_gt.vertex(3))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001networkx\u7684\u6027\u80fd\u4f18\u5316<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u5927\u89c4\u6a21\u56fe\u6570\u636e\uff0cnetworkx\u7684\u6027\u80fd\u53ef\u80fd\u4f1a\u6709\u6240\u9650\u5236\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u6027\u80fd\u4f18\u5316\u7684\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
\u4f7f\u7528\u7a00\u758f\u77e9\u9635<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u7a00\u758f\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528scipy\u7684\u7a00\u758f\u77e9\u9635\u6765\u5b58\u50a8\u56fe\u6570\u636e\uff1a<\/p>\n<\/p>\n
import scipy.sparse as sp<\/p>\n\u5c06\u56fe\u8f6c\u6362\u4e3a\u7a00\u758f\u77e9\u9635<\/strong><\/h2>\nadj_matrix = nx.to_scipy_sparse_matrix(G)<\/p>\n
\u5c06\u7a00\u758f\u77e9\u9635\u8f6c\u6362\u4e3a\u56fe<\/strong><\/h2>\nG_new = nx.from_scipy_sparse_matrix(adj_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528\u5e76\u884c\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u8ba1\u7b97\u5bc6\u96c6\u578b\u64cd\u4f5c\uff0c\u53ef\u4ee5\u4f7f\u7528\u5e76\u884c\u8ba1\u7b97\u6765\u63d0\u9ad8\u6027\u80fd\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528joblib\u5e93\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
from joblib import Parallel, delayed<\/p>\n\u5e76\u884c\u8ba1\u7b97\u8282\u70b9\u7684\u5ea6<\/strong><\/h2>\ndegrees = Parallel(n_jobs=-1)(delayed(G.degree)(n) for n in G.nodes())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
networkx\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u64cd\u4f5c\u548c\u7b97\u6cd5\u3002\u65e0\u8bba\u662f\u8fdb\u884c\u57fa\u672c\u7684\u56fe\u64cd\u4f5c\uff0c\u8fd8\u662f\u590d\u6742\u7684\u56fe\u7b97\u6cd5\uff0cnetworkx\u90fd\u80fd\u6ee1\u8db3\u4f60\u7684\u9700\u6c42\u3002\u901a\u8fc7\u4e0e\u5176\u4ed6Python\u5e93\u96c6\u6210\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u6269\u5c55\u5176\u529f\u80fd\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u5b89\u88c5\u548c\u4f7f\u7528networkx\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u5728Python3\u4e2d\u5b89\u88c5networkx\u5e93\uff1f<\/strong>
\u8981\u5728Python3\u4e2d\u5b89\u88c5networkx\u5e93\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u5305\u7ba1\u7406\u5de5\u5177pip\u3002\u5728\u547d\u4ee4\u884c\u6216\u7ec8\u7aef\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1a <\/p>\npip install networkx\n<\/code><\/pre>\n\u786e\u4fdd\u60a8\u7684pip\u7248\u672c\u662f\u6700\u65b0\u7684\uff0c\u5982\u679c\u4e0d\u786e\u5b9a\uff0c\u53ef\u4ee5\u901a\u8fc7\u8fd0\u884cpip install --upgrade pip<\/code>\u6765\u66f4\u65b0\u5b83\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u5728Python\u73af\u5883\u4e2d\u8f93\u5165import networkx<\/code>\u6765\u9a8c\u8bc1\u662f\u5426\u5b89\u88c5\u6210\u529f\u3002<\/p>\n\u5728\u5b89\u88c5networkx\u4e4b\u524d\uff0c\u6211\u9700\u8981\u5b89\u88c5\u5176\u4ed6\u4f9d\u8d56\u5e93\u5417\uff1f<\/strong>
\u901a\u5e38\u60c5\u51b5\u4e0b\uff0cnetworkx\u5e93\u4e0d\u9700\u8981\u989d\u5916\u7684\u4f9d\u8d56\u5e93\u5373\u53ef\u8fd0\u884c\u3002\u5b83\u672c\u8eab\u662f\u4e00\u4e2a\u72ec\u7acb\u7684\u56fe\u5f62\u5e93\uff0c\u63d0\u4f9b\u4e86\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u7814\u7a76\u590d\u6742\u7f51\u7edc\u7684\u5de5\u5177\u3002\u4e0d\u8fc7\uff0c\u5982\u679c\u60a8\u8ba1\u5212\u4f7f\u7528networkx\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\uff08\u5982numpy\u6216scipy\uff09\u4e00\u8d77\u4f7f\u7528\uff0c\u5efa\u8bae\u540c\u65f6\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff0c\u4ee5\u4fbf\u5145\u5206\u5229\u7528\u5b83\u4eec\u7684\u529f\u80fd\u3002<\/p>\n\u6211\u5728\u5b89\u88c5networkx\u65f6\u9047\u5230\u6743\u9650\u95ee\u9898\uff0c\u8be5\u5982\u4f55\u89e3\u51b3\uff1f<\/strong>
\u5982\u679c\u5728\u5b89\u88c5\u8fc7\u7a0b\u4e2d\u9047\u5230\u6743\u9650\u95ee\u9898\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528\u7ba1\u7406\u5458\u6743\u9650\u8fd0\u884c\u547d\u4ee4\u884c\u3002\u5728Windows\u4e0a\uff0c\u53ef\u4ee5\u53f3\u51fb\u201c\u547d\u4ee4\u63d0\u793a\u7b26\u201d\u5e76\u9009\u62e9\u201c\u4ee5\u7ba1\u7406\u5458\u8eab\u4efd\u8fd0\u884c\u201d\u3002\u5728Linux\u6216macOS\u4e0a\uff0c\u53ef\u4ee5\u5728\u547d\u4ee4\u524d\u52a0\u4e0asudo<\/code>\u6765\u83b7\u5f97\u7ba1\u7406\u5458\u6743\u9650\uff0c\u547d\u4ee4\u5982\u4e0b\uff1a <\/p>\nsudo pip install networkx\n<\/code><\/pre>\n\u6b64\u5916\uff0c\u60a8\u4e5f\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u865a\u62df\u73af\u5883\uff08\u5982venv\u6216conda\uff09\u6765\u9694\u79bb\u9879\u76ee\u4f9d\u8d56\uff0c\u4ece\u800c\u907f\u514d\u6743\u9650\u95ee\u9898\u3002<\/p>\n
networkx\u5e93\u7684\u4e3b\u8981\u529f\u80fd\u662f\u4ec0\u4e48\uff1f<\/strong>
networkx\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u4e13\u95e8\u7528\u4e8e\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u5206\u6790\u590d\u6742\u7f51\u7edc\u3002\u5b83\u652f\u6301\u591a\u79cd\u56fe\u5f62\u7c7b\u578b\uff0c\u5305\u62ec\u65e0\u5411\u56fe\u3001\u6709\u5411\u56fe\u548c\u52a0\u6743\u56fe\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528\u5b83\u6765\u8ba1\u7b97\u7f51\u7edc\u7684\u5404\u79cd\u5c5e\u6027\uff0c\u5982\u6700\u77ed\u8def\u5f84\u3001\u8fde\u901a\u6027\u3001\u4e2d\u5fc3\u6027\u7b49\u3002\u6b64\u5916\uff0cnetworkx\u8fd8\u652f\u6301\u56fe\u5f62\u7684\u53ef\u89c6\u5316\uff0c\u65b9\u4fbf\u7528\u6237\u76f4\u89c2\u5730\u7406\u89e3\u7f51\u7edc\u7ed3\u6784\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python3\u4e2d\u5b89\u88c5networkx\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u6b65\u9aa4\uff1a\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\u3001\u4f7f\u7528Anaconda\u8fdb\u884c\u5b89\u88c5 […]","protected":false},"author":3,"featured_media":1181978,"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\/1181972"}],"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=1181972"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1181972\/revisions"}],"predecessor-version":[{"id":1181979,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1181972\/revisions\/1181979"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1181978"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1181972"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1181972"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1181972"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"python3\u5982\u4f55\u5b89\u88c5networkx\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25130130\/8f28a767-213e-46fd-8586-fc3b6d5ce3ef.webp\")
<\/p>\n