A brief introduction to bibliometrix（四）
包网址：https://www.bibliometrix.org
教程网址：https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html
网络图特征的描述性分析
函数networkStat计算几个汇总统计信息。
特别是，从文献矩阵（或igraph对象）开始，计算两组描述性度量：
网络的汇总统计；The summary statistics of the network; 顶点中心性和声望的主要指标。The main indices of centrality and prestige of vertices.
# An example of a classical keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis =
"co-occurrences", network = "keywords", sep = ";"
)
netstat <- networkStat(NetMatrix)
网络的汇总统计
这组统计数据允许描述网络的结构属性：
大小是组成网络的顶点数量；Size is the number of vertices composing the network;
密度是网络中所有可能边的当前边的比例；Density is the proportion of present edges from all possible edges in the network;
传递性是三角形与连通三元组的比率；Transitivity is the ratio of triangles to connected triples;
直径是网络中最长的测地线距离（两个节点之间最短路径的长度）；Diameter is the longest geodesic distance (length of the shortest path between two nodes) in the network;
度分布是顶点度的累积分布；Degree distribution is the cumulative distribution of vertex degrees;
度中心性是整个网络的归一化程度；Degree centralization is the normalized degree of the overall network
贴近中心化是网络中顶点平均测地线距离的归一化倒数；Closeness centralization is the normalized inverse of the vertex average geodesic distance to others in the network;
特征向量中心化是图矩阵的第一个特征向量；Eigenvector centralization is the first eigenvector of the graph matrix;
介数中心化是通过顶点的测地线的归一化数量；Betweenness centralization is the normalized number of geodesics that pass through the vertex;
平均路径长度是网络中每对顶点之间最短距离的平均值。Average path length is the mean of the shortest distance between each pair of vertices in the network.
names(netstat$network)
顶点的中心性和威望的主要指标 The main indices of centrality and prestige of vertices
这些度量有助于识别网络中最重要的顶点以及连接到第三个顶点的两个顶点的倾向。
networkStat返回的顶点级别的统计信息是：
Degree centrality Closeness centrality measures how many steps are required to access every other vertex from a given vertex; Eigenvector centrality is a measure of being well-connected connected to the well-connected; Betweenness centrality measures brokerage or gatekeeping potential. It is (approximately) the number of shortest paths between vertices that pass through a particular vertex; PageRank score approximates probability that any message will arrive to a particular vertex. This algorithm was developed by Google founders, and originally applied to website links; Hub Score estimates the value of the links outgoing from the vertex. It was initially applied to the web pages; Authority Score is another measure of centrality initially applied to the Web. A vertex has high authority when it is linked by many other vertices that are linking many other vertices; Vertex Ranking is an overall vertex ranking obtained as a linear weighted combination of the centrality and prestige vertex measures. The weights are proportional to the loadings of the first component of the Principal Component Analysis.
names(netstat$vertex)
要总结networkStat函数的主要结果，请使用通用函数summary。
它通过几个表格显示有关网络和顶点描述的主要信息。
summary接受一个额外的参数。k是一个格式值，指示每个表的行数。选择k=10，您决定查看前10个顶点。
summary(netstat, k=10)
##
##
## Main statistics about the network
##
## Size 475
## Density 0.024
## Transitivity 0.335
## Diameter 5
## Degree Centralization 0.301
## Average path length 2.743
##
可视化书目网络
所有文献网络都可以图形可视化或建模。
在这里，我们展示了如何使用函数networkPlot和VOSView软件可视化网络(https://www.vosviewer.com).
使用函数networkPlot或使用VOSview绘制由bibleoNetwork创建的网络。
networkPlot的主要参数是type。它表示网络地图布局：圆、kamada-kawai、mds等。选择type="vosView"，函数会自动：（i）将网络保存到一个名为"vosnetwork.net"的pajek网络文件中；（ii）启动一个将映射文件“vosnetwork.net”的VOSView实例。您需要使用参数vos. path声明VOSView软件所在文件夹的完整路径（即vos.path='c：/soft/VOSView'）。
国家间科学合作分析
# Create a country collaboration network
M <- metaTagExtraction(M, Field =
"AU_CO", sep = ";"
)
NetMatrix <- biblioNetwork(M, analysis =
"collaboration", network = "countries", sep = ";"
)
# Plot the network
net=networkPlot(NetMatrix, n = dim(NetMatrix)[
1], Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.7,cluster="none")
Co-Citation Network
# Create a co-citation network
# NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
# Plot the network
#net=networkPlot(NetMatrix, n = 30, Title = "Co-Citation Network", type = "fruchterman", size=T, remove.multiple=FALSE, labelsize=0.7,edgesize = 5)
Keyword co-occurrences
# Create keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis =
"co-occurrences", network = "keywords", sep = ";"
)
# Plot the network
net=networkPlot(NetMatrix, normalize=
"association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "fruchterman", size=T,edgesize = 5,labelsize=0.7)
共词分析：一个领域的概念结构 Co-Word Analysis: The conceptual structure of a field
共词分析的目的是使用文献集合中的共出现一词来映射框架的概念结构。分析可以通过降维技术进行，例如多维缩放（MDS）、对应分析（CA）或多重对应分析（MCA）。
在这里，我们展示了一个例子，使用函数概念结构来绘制字段的概念结构，并使用K-means聚类来识别表达常见概念的文档集群。结果绘制在二维地图上。
概念结构包括自然语言处理（NLP）例程（参见函数术语提取），从标题和摘要中提取术语。此外，它实现了波特词干算法，将屈折（或有时派生）的单词简化为词干、基部或词根形式。
# Conceptual Structure using keywords (method="CA")
CS <- conceptualStructure(M,field="ID", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)
历史直引网络-Historical Direct Citation Network
历史地图是由E. Garfield（2004）提出的图表，用于表示由文献收藏产生的最相关直接引用的时间网络地图。
该函数生成一个按时间顺序排列的直接引用网络矩阵，可以使用histPlot绘制：
# Create a historical citation network
options(width=130)
histResults <- histNetwork(M, min.citations = 1, sep = ";")
# Plot a historical co-citation network
net <- histPlot(histResults, n=15, size = 10, labelsize=5)
参考文献-Main Authors’ references (about bibliometrics)
Aria, M. & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007 (https://doi.org/10.1016/j.joi.2017.08.007).
Aria M., Misuraca M., Spano M. (2020) Mapping the evolution of social research and data science on 30 years of Social Indicators Research, Social Indicators Research. (DOI: )https://doi.org/10.1007/s11205-020-02281-3)
Aria, M., Cuccurullo, C., D’Aniello, L., Misuraca, M., & Spano, M. (2022). Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in Italy. Sustainability, 14(6), 3643, (https://doi.org/10.3390/su14063643).
Aria M., Alterisio A., Scandurra A, Pinelli C., D’Aniello B, (2021) The scholar’s best friend: research trends in dog cognitive and behavioural studies, Animal Cognition. (https://doi.org/10.1007/s10071-020-01448-2)
Cuccurullo, C., Aria, M., & Sarto, F. (2016). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains, Scientometrics, DOI: 10.1007/s11192-016-1948-8 (https://doi.org/10.1007/s11192-016-1948-8).
Cuccurullo, C., Aria, M., & Sarto, F. (2015). Twenty years of research on performance management in business and public administration domains. Presentation at the Correspondence Analysis and Related Methods conference (CARME 2015) in September 2015 (https://www.bibliometrix.org/documents/2015Carme_cuccurulloetal.pdf).
Sarto, F., Cuccurullo, C., & Aria, M. (2014). Exploring healthcare governance literature: systematic review and paths for future research. Mecosan (https://www.francoangeli.it/Riviste/Scheda_Rivista.aspx?IDarticolo=52780&lingua=en).
Cuccurullo, C., Aria, M., & Sarto, F. (2013). Twenty years of research on performance management in business and public administration domains. In Academy of Management Proceedings (Vol. 2013, No. 1, p. 14270). Academy of Management (https://doi.org/10.5465/AMBPP.2013.14270abstract).
广告-新课推荐
高分文章新方法-基于R语言的动态预测模型课程第三期
开课目的及前言
预测模型类文章目前总结起来发展经历了以下三个阶段：
基于传统流行病学的列线图模型（本质都是cox回归及glm回归），简单的统计学分析模型，是模型依赖的方法，临床上实际情况很难满足其前提假设，实际效果不好。
基于机器学习/深度学习的预测模型的构建（在数据上提高了维度，在算法上引入了机器学习），虽然算法上引入了机器学习模型，处理数据更加灵活，模型的假设也更少。但是在使用的数据上还是患者的一次基线数据进行预测，与临床实际不符。
基于纵向数据的动态预测模型（基于纵向多次随访数据，模型应用联合模型等动态预测模型方法），应用患者的多次随访数据对最终的生存结果进行预测，从数据和方法上都更类似于临床实际。
考虑到动态预测模型有以下特点，因此必然是后续高分文章的必备方法：
数据上必须有同一个患者的多次随访数据，相对于既往横断面一次基线数据，数据的收集难度更大，而且动态预测模型需拟合纵向的线性混合模型，因此需要的数据量较大。这就提示我们如果能收集到如上数据更加容易发高分文章。
应用方法学动态预测模型需首先掌握普通生存分析及普通预测模型的方法，并且还需要熟悉纵向数据分析的广义线性混合模型，再次基础上还需要掌握tidyverse语法基础来将自己的数据转换为满足函数要求的纵向数据，另外对于联合模型，模型的结合形式及变量选择也均需要从临床背景及统计学方法考虑。
授课老师
1 灵活胖子
双一流学校肿瘤学博士毕业，目前就职于国内五大肿瘤中心之一。科研方向为真实世界研究，生物信息学分析及人工智能研究。目前以第一或共同第一作者身份发表SCI论文10余篇，累计IF50+。目前与国内多个院校及医院有科研合作。联合翻译小组同学，在国内第一次将jmbayes2及dynamicLM全文翻译为中文并在公众号发表。
2 Rio
医学博士，临床医生。发表中英文文章 10 余篇。R 与 python 爱好者。
