图论〔Graph Theory〕以图为研究对象,是离散数学的重要内容。图论不仅与拓扑学、计算机数据结构和算法密切相关,而且正在成为机器学习的关键技术。 图论中所说的图,不是指图形图像(image)或地图(map),而是指由顶点(vertex)和连接顶点的边(edge)所构成的关系结构。 图提供了一种处理关系和交互等抽象概念的更好的方法...
Graph.add_node(node_for_adding, **attr) Graph.add_nodes_from(nodes_for_adding, **attr) Graph.remove_node(n) Graph.remove_nodes_from(nodes) # 顶点(node)的操作# 向图中添加顶点G1.add_node(1)# 向 G1 添加顶点 1G1.add_node(1, name='n1', weight=1.0)# 添加顶点 1,定义 name, weig...
Graph() 类、DiGraph() 类、MultiGraph() 类和 MultiDiGraph() 类分别用来创建:无向图、有向图、多图和有向多图。定义和例程如下: class Graph(incoming_graph_data=None, **attr) import networkx as nx # 导入 NetworkX 工具包 # 创建 图 G1 = nx.Graph() # 创建:空的 无向图 G2 = nx.DiGraph()...
nx.average_shortest_path_length(FG) # Average shortest path length for ALL paths in the Graph 输出: 2.36984126984127 nx.average_degree_connectivity(FG) # For a node of degree k - What is the average of its neighbours' degree? 输出: {1:...
俗话说一图胜千言。但是“图”(Graph)说的远不止于此。以图形式呈现的数据可视化能帮助我们获得见解,并基于它们做出更好的数据驱动型决策。 但要真正理解图是什么以及为什么使用它们,我们需要理解一个称为图论(Graph Theory)的概念。理解它可以使我们成为更好的程序员。
Caley研究了微分学的特定分析形式来研究树。这在理论化学中有许多含义。这也导致了枚举图论(enumerative graph theory)的发明。不管怎么说,“图”这个术语是由Sylvester在1878年引入的,他在“量子不变量”与代数和分子图的协变量之间进行了类比。 1941年,Ramsey致力于着色问题,这产生了另一个图论的分支 - 极值图论(...
A simple python package for graph theory that supports multi-edges, hyper-edges, looped-edges and every other combination of these. - vaibhavkarve/multihypergraph
Name: networkxVersion: 2.4Summary: Python package for creating and manipulating graphs and networksHome-page: http://networkx.github.io/Author: Aric HagbergAuthor-email: hagberg@lanl.govLicense: BSDLocation: c:\anaconda3\lib\site-packagesRequires: decoratorRequired-by: scikit-image ...
This chapter gives facts and theorems from linear algebra and graph theory that we use in the following chapters. We include a brief introduction to Python programming with further references for more extensive study. Python codes are provided to help you learn the basics and to give you a ...
Structural Graph Theory Calculator StructuralGT is designed as an easy-to-use python-based application for applying graph theory (GT) analysis to structural networks of a wide variety of material systems. This application converts digital images of nano-/micro-/macro-scale structures into a graph ...