NetworkX 2.6rc1详细参考手册：涵盖140+算法与数据结构

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《NetworkXReference》是一本详尽记录网络分析与图论算法的900多页专业参考书，由Aric Hagberg、Dan Schult和Pieter Swart三位作者编著，于2021年2月发布。该书主要围绕Python的网络分析库NetworkX展开，涵盖了从基础概念到高级算法的广泛内容。 **1. 引言与基础** - **NetworkX基础知识**：介绍NetworkX的核心概念，包括图的基本定义，以及如何在Python中创建、操作和理解图。 - **图的创建**：讲解如何通过多种方式（如节点和边的列表、邻接矩阵、邻接列表等）在NetworkX中构建图形结构。 - **图的报告与展示**：涉及图的各种属性查看，以及如何输出和可视化图的结构和特性。 - **算法基础**：提供对基本图算法的概述，如遍历、搜索、最短路径等。 **2. 图类型与视图** - **选择合适的图类**：指导读者根据具体需求选择适合的图模型，如无向图、有向图或混合图。 - **基本图类型**：介绍不同类型图的特点，如树、森林、环、完全图等。 - **图视图**：区分核心视图（如原图、子图等）、过滤视图（仅显示特定属性的节点和边）的概念。 - **图数据结构**：深入解析NetworkX内部如何存储和处理图数据。 **3. 算法详解** - **近似算法与启发式**：探讨在求解复杂问题时使用的优化方法。 - **图的统计特性**：涉及度分布、聚类系数、 assortativity（同质性）等度量。 - **特定主题算法**：如星形结构、分块、连通性分析（桥、连通分量、组件）、核心-壳结构、覆盖问题、循环检测、切点等。 - **流与容量问题**：如最大流、最小割等经典网络分析问题。 - **图的周期性和距离计算**：探讨距离相关算法，如欧拉路径、最短路径、中心性测量（如度中心性、接近中心性）。这本书不仅提供了丰富的理论知识，还包含了实用的代码示例，对于任何从事网络科学、社交网络分析或数据科学工作的Python开发者来说，都是不可或缺的参考资料。通过深入阅读和实践，读者可以掌握如何在实际项目中高效地使用NetworkX进行图论分析和算法实现。

NetworkX Reference, Release 2.6rc1.dev0

Simple graph information is obtained using object-attributes and methods. Reporting typically provides views in-

stead of containers to reduce memory usage. The views update as the graph is updated similarly to dict-views. The

objects nodes, edges and adj provide access to data attributes via lookup (e.g. nodes[n], edges[u, v],

adj[u][v]) and iteration (e.g. nodes.items(), nodes.data('color'), nodes.data('color',

default='blue') and similarly for edges) Views exist for nodes, edges, neighbors()/adj and de-

gree.

For details on these and other miscellaneous methods, see below.

Subclasses (Advanced):

The Graph class uses a dict-of-dict-of-dict data structure. The outer dict (node_dict) holds adjacency information

keyed by node. The next dict (adjlist_dict) represents the adjacency information and holds edge data keyed by

neighbor. The inner dict (edge_attr_dict) represents the edge data and holds edge attribute values keyed by attribute

names.

Each of these three dicts can be replaced in a subclass by a user de󰝘ned dict-like object. In general, the

dict-like features should be maintained but extra features can be added. To replace one of the dicts create a

new graph class by changing the class(!) variable holding the factory for that dict-like structure. The vari-

able names are node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory, adjlist_outer_dict_factory,

edge_attr_dict_factory and graph_attr_dict_factory.

node_dict_factory [function, (default: dict)] Factory function to be used to create the dict containing node at-

tributes, keyed by node id. It should require no arguments and return a dict-like object

node_attr_dict_factory: function, (default: dict) Factory function to be used to create the node attribute dict

which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like

object

adjlist_outer_dict_factory [function, (default: dict)] Factory function to be used to create the outer-most dict

in the data structure that holds adjacency info keyed by node. It should require no arguments and return a

dict-like object.

adjlist_inner_dict_factory [function, (default: dict)] Factory function to be used to create the adjacency list dict

which holds edge data keyed by neighbor. It should require no arguments and return a dict-like object

edge_attr_dict_factory [function, (default: dict)] Factory function to be used to create the edge attribute dict

which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like

object.

graph_attr_dict_factory [function, (default: dict)] Factory function to be used to create the graph attribute dict

which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like

object.

Typically, if your extension doesn’t impact the data structure all methods will inherit without issue except:

to_directed/to_undirected. By default these methods create a DiGraph/Graph class and you proba-

bly want them to create your extension of a DiGraph/Graph. To facilitate this we de󰝘ne two class variables that

you can set in your subclass.

to_directed_class [callable, (default: DiGraph or MultiDiGraph)] Class to create a new graph structure in the

to_directed method. If None, a NetworkX class (DiGraph or MultiDiGraph) is used.

to_undirected_class [callable, (default: Graph or MultiGraph)] Class to create a new graph structure in the

to_undirected method. If None, a NetworkX class (Graph or MultiGraph) is used.

Subclassing Example

Create a low memory graph class that e󰝗ectively disallows edge attributes by using a single attribute dict for all

edges. This reduces the memory used, but you lose edge attributes.

10 Chapter 2. Graph types

NetworkX Reference, Release 2.6rc1.dev0

>>> class ThinGraph(nx.Graph):

... all_edge_dict = {"weight": 1}

...

... def single_edge_dict(self):

... return self.all_edge_dict

...

... edge_attr_dict_factory = single_edge_dict

>>> G = ThinGraph()

>>> G.add_edge(2, 1)

>>> G[2][1]

{'weight': 1}

>>> G.add_edge(2, 2)

>>> G[2][1] is G[2][2]

True

Please see ordered for more examples of creating graph subclasses by overwriting the base class dict with a

dictionary-like object.

Methods

Adding and removing nodes and edges

Graph.__init__([incoming_graph_data]) Initialize a graph with edges, name, or graph attributes.

Graph.add_node(node_for_adding, **attr) Add a single node node_for_adding and update

node attributes.

Graph.add_nodes_from(nodes_for_adding,

**attr)

Add multiple nodes.

Graph.remove_node(n) Remove node n.

Graph.remove_nodes_from(nodes) Remove multiple nodes.

Graph.add_edge(u_of_edge, v_of_edge, **attr) Add an edge between u and v.

Graph.add_edges_from(ebunch_to_add, **attr) Add all the edges in ebunch_to_add.

Graph.add_weighted_edges_from(ebunch_to_add)Add weighted edges in ebunch_to_add with speci󰝘ed

weight attr

Graph.remove_edge(u, v) Remove the edge between u and v.

Graph.remove_edges_from(ebunch) Remove all edges speci󰝘ed in ebunch.

Graph.update([edges, nodes]) Update the graph using nodes/edges/graphs as input.

Graph.clear() Remove all nodes and edges from the graph.

Graph.clear_edges() Remove all edges from the graph without altering nodes.

networkx.Graph.__init__

Graph.__init__(incoming_graph_data=None, **attr)

Initialize a graph with edges, name, or graph attributes.

Parameters

incoming_graph_data [input graph (optional, default: None)] Data to initialize graph. If None

(default) an empty graph is created. The data can be an edge list, or any NetworkX graph

object. If the corresponding optional Python packages are installed the data can also be a

NumPy matrix or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.

attr [keyword arguments, optional (default= no attributes)] Attributes to add to graph as key=value

2.2. Basic graph types 11

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NetworkX 2.6rc1详细参考手册：涵盖140+算法与数据结构

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cannot import name 'hamiltonian_path' from 'networkx.algorithms.approximation' (C:\Users\86182\anaconda3\lib\site-packages\networkx\algorithms\approximation\__init__.py)

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cannot import name 'hamiltonian_path' from 'networkx.algorithms.approximation' (C:\Users\86182\anaconda3\lib\site-packages\networkx\algorithms\approximation\init.py)