NetworkX参考手册2.4版：图论与算法详解

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"networkx_reference.pdf" 这是一份关于NetworkX的参考手册，NetworkX是一个在Python中用于创建、操作和研究复杂网络结构的库。手册涵盖了从基础概念到高级算法的广泛内容，适合学习和参考。 1. NetworkX基础知识 NetworkX的基本概念包括图（Graphs）的概念，它是一个节点（Vertices）和边（Edges）的集合。图可以是有向的（Directed），无向的（Undirected），有权重的（Weighted）或者无权重的（Unweighted）。此外，手册还介绍了如何创建和操作这些图。 2. 图的创建与报告创建图可以通过直接添加节点和边，或者通过加载数据集来完成。报告图的信息包括节点数量、边数量、连通性等。手册详细讲解了各种操作方法。 3. 算法 NetworkX提供了丰富的图算法，如最短路径算法（Shortest Path）、聚类系数计算（Clustering Coefficient）、中心度（Centrality）测量、社区检测（Community Detection）、连通性分析（Connectivity）等。这些算法涵盖了网络分析中的许多重要方面。 4. 绘图手册还涉及了如何使用NetworkX绘制图形，以便直观地展示网络结构。可以定制节点和边的属性，以及布局方式。 5. 数据结构 NetworkX使用不同的数据结构来存储和操作图，如邻接列表（Adjacency List）、邻接矩阵（Adjacency Matrix）等。理解这些数据结构有助于优化算法性能。 6. 不同类型的图包含多种图类型，如简单的无向图、有向图、多图（MultiGraph）和多重图（MultiDiGraph），每种类型都有其特定的用途和操作方法。 7. 高级算法高级算法包括但不限于：近似算法和启发式算法、同质性分析（Assortativity）、星状图（Asteroidal）、二分图（Bipartite）、边界（Boundary）、桥（Bridges）、核心数（Cores）、覆盖（Covering）、周期（Cycles）、切割（Cuts）等。这份手册是学习和使用NetworkX的强大工具，无论你是初学者还是经验丰富的研究人员，都能从中找到所需的信息。通过深入学习，你可以掌握网络分析的关键技能，从而在社交网络分析、生物网络分析、复杂系统建模等领域发挥重要作用。

NetworkX Reference, Release 2.4

(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 degree.

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 deﬁned 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

attributes, 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 probably

want them to create your extension of a DiGraph/Graph. To facilitate this we deﬁne 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.

Examples

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

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

>>> class ThinGraph(nx.Graph):

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

(continues on next page)

10 Chapter 2. Graph types

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