显着性检验
对社交网络分析和 R 非常陌生（在线阅读了一些介绍，自己创建了一些图表），但我非常渴望学习不同的应用方法。也就是说，应用于网络的常见显着性测试是什么，更具体地说，如果两个网络图“在统计上不同”？
如果您能向我提供任何帮助/教程（尤其是 R 方面的帮助/教程），我将不胜感激！
谢谢，
Very new to Social Network Analysis and R (read a few introductions online, created a few graphs on my own) but I am very eager to learn different ways to apply it. That said, what are common significance tests applied to networks, and more specifically, if two network graphs are "statistically different?"
Any help/tutorials you can point me to - especially in R - will be very much appreciated!
Thanks,
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首先，R 中的图形模型任务视图（在 CRAN 上）列出了可能主要与图论或任何应用程序相关的每个 R 包比如社交网络分析。
（任务视图只是按领域/应用程序（例如，财务）或按技术（例如，回归）相关的包的集合）。
如果您对网络建模相当陌生，并且没有图论背景，那么我建议您使用 R 包 netmodels（但其价值不仅限于新手）。 netmodels 本质上是一组名为 igraph 的 C 库中低级函数的包装器，它具有 R 绑定（也称为 igraph）。
例如，使用 netmodels，您只需
在网络上调用该函数，它将返回几个关键网络参数的列表以及每个网络的值（例如，节点数、数字边数、平均度、平均路径长度等）
因此，通过使用网络模型，您可以快速获得网络的高级描述，而无需根据“关节点”、“关节点”、“特征向量中心性”和“共引耦合”等基本图论组件，自己计算这些高级描述性参数。
其他三个类似的包含较低级图论函数的 R 包是 inetwork、RSiena 和 sna。
关于网络的“显着性测试”。我相信分析是针对所研究网络的关键参数/属性的统计显着性。
那么这些关键参数是什么呢？也许对此已经达成共识，但更可能的是这取决于您试图解决的特定问题。这是我几乎总是计算的关键网络参数列表（我将尝试在每个属性旁边列出一个函数及其 R 包）：
累积度分布（计算其边缘或链接，对于每一个
网络中的节点，让你
估计节点与边的比率）R 函数：netmodels 包中的plot.c. Degree
Krackhardt 连接性（返回从 0 到 1 的分数） R 函数：
sna 包中的连通性
平均路径长度（最短有向或无向）路径之间
节点 R 函数：average.path.length
在 igraph 包中
First, the Graphical Models in R Task View, (on CRAN) lists probably every R Package that is primarily related to graph theoretics or any application like social network analysis.
(Task Views are just collections of Packages related either by domain/application (e.g., Finance) or by technique (e.g., Regression)).
If you are fairly new to network modeling, and you don't have a background in graph theory, let me suggest the R Package netmodels (the value is not limited to novices though). netmodels is in essence a set of wrappers around lower-level functions in a C library called igraph, which has R bindings (also called igraph).
So for instance, using netmodels you can just call the function
on your network and it will return a list several key network parameters and that network's value for each (for instance, number of nodes, number of edges, average degree, average path length, etc.)
So by using netmodels you can quickly get a high-level description of your network without having to calculate those high-level descriptive parameters yourself from 'articulation points the basic graph theoretic components like 'articulation points', 'eigenvectors centrality', and 'Cocitation coupling'.
Three other similar R Packages which wrap lower-level graph theoretic functions are inetwork, RSiena, and sna.
Regarding "significance tests" for networks. I believe that analysis is directed to the statistical significance of key parameters/attributes of the networks under study.
So what are those key parameters? Perhaps there's a consensus on this, but more likely it depends on the particular problem you are trying to solve. Here's my list of key network parameters i nearly always calculate (i'll try to list a function and its R Package next each attribute):
cumulative degree distribution (count its edges, or links, for every
node in the network, let's you
estimate nodes-to-edges ratio) R Function: plot.c.degree in package netmodels
Krackhardt connectedness (returns a score from 0 to 1) R Function:
connectedness in package sna
average path length (shortest directed or undirected) path between
nodes R Function: average.path.length
in package igraph