这是为了M大学的network in python（https://github.com/sambhipiyush/Applied-Social-Network-Analysis-in-Python-University-of-Michigan） 的学习而开始的项目。 这个项目主要是几个方面的内容：1 是network的现象以及其研究价值；2 是python是如何定义network的（python如何表示network）； 3 network的相关measurement （为了防止忘记MD文件规则，（我自己）可以浏览 [MD认识与入门]（https://sspai.com/post/25137））
- 尽管network概念并不是一个新的事物，但是近年来network终于在大数据环境下获得新的生命力。如果用network观点来观察我们的工作（或者其他）对象， 我们发现很多现象都可以看作是network。比如， social network 在一个班级里，老师和同学之间的交流构成一个网络。 transportionion network 公交车在各个站点之间运行构成一个网络。 webpage network 比如网络上blogs,一个blog 与其他blog相联系。这些blogs（如果是入M老师所讲的political blogs, 就会发现左翼的政治人员往往和左翼的人员相联系，右翼分子与右翼分子相联系） 就形成信息网络。
- 可以看出很多现象都可以用network来描述，这就是研究network最主要的动机。可以想象，如果能够把我们所感兴趣的想象描述成network，那么我们就可以借助network的知识来增进我们对该现象的理解
python 开发团队开发了networkX库，可供network 研究。在具体学习networkX之前，需要解决的问题是，python 如何定义network。
- 从前面的现象来看，network主要特征是由能够形成联系（link, connection, dependency）的对象，以及多个这种对象形成的link, connection or dependency 构成。比如班级里面形成联系的对象是老师 和学生（当然还有其他事物对象也能形成network，但是这里假定我们只关心老师和学生群体），他们互动形成了联系，比如老师对每个学生的作业修改工作形成了老师和学生的联系，比如老师和同学们之间的朋 友关系形成的网络。再比如blogs例子中，blog的主人是形成关系的对象，所形成的关系是connections between blogs through URLs.
- 那么如何形成一般性的network定义？
- 在图论中，将这些关系定义为nodes 和 edges。nodes 就是形成联系的对象，edges表示connection,link or dependency.
A Network , which we also call a Graph, is a representation of connections among different sets. This is a network between people and the edges here represent friendship, marital ties and family ties among 2,200 people Nodes : People Edges : Friendship, marital, or family ties 那么关键在python 如何基于nodes 和edges 来处理network, networkx。 'import networkx as nx
G = nx.Graph() G.add_edges('A', 'B') G.add_edges('B', 'C') '
-undirected network 如果edges 没有方向性，或者说edges方向没有什么意义。
These are undirected networks, meaning the edges don't have any direction, or that the direction of the edge is not really important -directed network edges 方向具有重要意义。(我目前主要关注这一类，因为这一类与我感兴趣的现象相关) These are directed networks, meaning the edges have direction, or that the direction of the edge is really important In NetworkX we use the class DiGraph (Directed Graph) to represent these types of networks
- weighted network edges 被赋予了一定的权重。
Weighted Network is a network where edges are assigned a particular weight, typically a numerical weight, that has some meaning in the relationship between the two nodes The intuition here is that in some networks Not all relationships are equal Some edges carry different weight than others, so we want a way of capturing this A network where the nodes are people and then the edges represent how many times they had lunch together. These are coworkers In NetworkX, we can represent these types of networks also by using the class Graph But what have to do is when we add an edge, for example the edge A, B, we have to add an attribute weight as well import networkx as nx ' G = nx.Graph() G.add_edges('A', 'B', weight=6) G.add_edges('B', 'C', weight=13)' 可以看出network中edges是储存重要信息的对象，通过edges的性质来对network进行分类的。networkX中可以按照不同的类型的network构建不同的类，这些类主要是class Graph 和DiGraph 两个我感兴趣的类。在不同的类中，主要通过类的方法为edges 添加属性 attributes来描述network的。 比如，加weight，加relation 等属性 Edges can carry many other labels or Attributes Relation Friend Family Coworker Neighbor
'import networkx as nx G = nx.Graph() G.add_edges('A', 'B', relation='friend') G.add_edges('B', 'C', relation='coworker') G.add_edges('D', 'E', relation='family') '
*更具介绍，还有一类network是MultiGraph network. 特征是：
A pair of nodes can have different types of relationships simultaneously Intuition for multigraphs is that for a single pair of of nodes, there is no reason why they shouldn't be able to have many different relationships at the same time MultiGraphs Network is a network where multiple edges can connect the same pair of nodes. They're also called parallel edges 这种MutilGraph 在networkX中有 MultiGraph类来描述 ' import networkx as nx G = nx.MultiGraph() G.add_edges('A', 'B', relation='friend') G.add_edges('A', 'B', relation='neighbor') G.add_edges('G', 'F', relation='family') G.add_edges('G', 'F', relation='coworker') ' *