是来自b站up主同济子豪兄的中文精讲，自己做来总结给自己学习滴，如果感兴趣的话可以去b站搜索或者去子豪兄的github图神经进行学习： https://github.com/TommyZihao/zihao_course/blob/main/CS224W/1-Intro.md
图是有节点（nodes or vertices)和连接(links or edges)组成
生活中的很多关系（relationship）都可以用图来表示
节点的类型以及节点之间可能存在的连接(关系）
How to build a graph:
What are nodes?
What are edges?
关键是如何确定节点和边，你想要解决什么问题
Choice of the proper network representation of a given doman/problem determines our ability to use networks successfully:
In some cases, there is a unique, unambiguous representation
(有些时候，本体图是唯一的、无歧义的)
In other cases,the representation is by no means unique
The way you assign links will determine the nature of the question you can study.
Instance: 红楼梦知识图谱
（https://grapheco.org/InteractiveGraph/dist/examples/example1.html )
异质图（heterogeneous graph) :
Nodes with node types (结点种类可以不同）
Edges with relation types 
Node type 
Relation type 
两类结点之间的关系抽象成二分图
可以展开二分图（根据关系连接）
如 U中 1、2、3都连到A，把他们连在一起
Node degree 来表示结点重要度、中心度、枢纽度
Source 、Sink
in-degree(入度）、out-degree（出度）
结点的度怎么计算（degree)
为什么要把图表示成矩阵的形式？
邻接矩阵保留了图的全部信息，是全息的；计算机、算法都是通过矩阵的运算
（图翻译为计算机的语言）
drawback: sparse matrix
可以看到用邻接矩阵很浪费空间，所以一般用连接列表和邻接列表
Multigraph：
结点与结点之间存在多种关系（能吃、不能吃、推荐吃）
undirected graph：
总有一条路能让图的任意两个结点之间触达，称之为”虽远必诛“ （connected components)
connected components: Any two vertices can be jointed by a path.
A disconnected graph is made up by two or more connected components(连通域）.
最大的连通域（Giant Component)
instance:
directed graph：
有向图中，任意两个节点可以相处触达：
Strongly connected directed graph(强连通图）
has a path from each node to every other node and vice versa (e.g., A-B path and B-A path)
Weakly connected directed graph(弱连通图）
is connected if we disregrad the edge directions
instance:
subset is strong directed graph can be named strongly connected components(SCCs)
