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#1社交网络分析方法
社会网络分析法是一种社会学研究方法。社会学理论认为，社会不是由个人组成的，而是由网络组成的，网络包含节点以及节点之间的关系。社会网络分析法通过分析网络中的关系来探讨网络的结构和属性特征，包括网络中的个体属性和网络的整体属性。网络的个体属性分析包括3360度中心性、邻近中心性等。网络的整体属性分析包括小世界效应、小群体研究、凝聚子群等。
#2UCINET软件介绍
UCINET软件作为最流行的社交网络分析软件之一，经常被用来分析一维和二维数据。使用UCINET软件，您可以读取文本文件、KrackPlot、pajek、Negopy、VNA和其他格式的文件。它可以处理32，767个网络节点。当然，从实用的角度来看，当节点数在5000-10000之间时，有些程序会运行缓慢。
#3一个小案例
通常社交网络分析之前最重要的一步是找到原始数据。
比如研究国内学者在供应链领域的研究热点问题，学者可以选择CNKI作为文献数据库，以“供应链”为关键词或标题进行搜索，通过对文献的多次筛选，得到最有参考价值和学术水平的文献。
通过建立文献作者和文献关键词的矩阵，即作者和研究问题的2模式网络。这是我们做社交网络分析需要的原始数据。
接下来我们来做社交网络分析的具体操作。本案例中使用的数据来自参考文献2。
步骤1:导入原始数据方法一.
依次点击快捷栏左边第三个小图标：通过电子表格界面导入文本数据-加载一个数据文件-确定-全矩阵-保存-名称。
方法二
直接在软件中点击快捷栏左边第二个小图标——粘贴原始数据——点击保存保存数据——命名。
步骤2:双模式到单模式原因：
原始数据中，我们建立了包含文献作者和关键词的矩阵，其中，若该作者的文献中有某个关键词，那么我们就在这个作者所在行与该关键词所在列的交叉处，填上1。但是，如果我们要研究的是某个领域的研究热点，那就需要建立“关键词”-“关键词”的1-mode网络。
操作步骤：
Data―Affiliations（2-mode to 1-mode）―Input dataset（选择上一步后缀名为“.##h”的DL格式文件）―Column―OK。
步骤3:生成数值中心性操作步骤：.
Network―centrality―Degree―Inputdataset(选择上一步保存的后缀名为“Aff.##h”的文件)―OK。
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得到的结果如图：
第四步：画出中心度图形操作步骤：
Visualize―NetDraw―file―open―ucinetdataset―Network―“.##h”―OK。
得到结果如图：
第五步:画出最终中心度图形操作步骤：
Analysis―centralitymeasures―Set Node Sizes by―Degree。
得到结果如图：
第六步：分析结果通过第五步中生成的图我们可以看到，节点“应急能力”和“企业信誉”的图标面积最大，这表明，这两个节点的中心度高于其他节点，相应地，“应急能力”和“企业信誉”也是该领域论文中的热门关键词。
英文学习：
01 Social Network Analysis
Social network analysis is a method of sociological research. Sociological theory believes that society is not composed of individuals but of networks. The network contains nodes and the relationship between nodes. Analyze and explore the structure and attribute characteristics of the network, including the individual attributes in the network and the overall network attributes. The analysis of individual network attributes includes: point degree centrality, proximity centrality, etc.; the overall network attribute analysis includes small-world effects, small group research, Condensed subgroups and so on <1>.
02 Introduction of UCINET software
As one of the most popular social network analysis software, UCINET software is commonly used for one-dimensional and two-dimensional data analysis. UCINET software can read text files, KrackPlot, pajek, Negopy, VNA and other format files. It can handle 32767 network nodes. Of course, from the actual operation point of view, when the number of nodes is between 5,000 and 10,000, some programs will run very slowly <2>.
03 A small case
Usually, the most important step before conducting social network analysis is to find the original data.
For example, to study research hotspots of domestic scholars in the field of supply chain, scholars can choose CNKI as a document database, search with "supply chain" as a key word or title, and obtain the best results through multiple screenings of documents. The most academic literature with reference value.
By establishing a matrix of literature authors and literature keywords, it is a 2-mode network of authors and research questions. This is the raw data we need for social network analysis.
Below, we carry out the specific operation of social network analysis. The data used in the case comes from reference <2>.
The first step: Import the original data.
Method 1: Click the third small icon from the left in the shortcut bar in turn, importtext data via spreadsheet interface ― Load a data file (select the EXCEL file of the original data at this time) ― OK ― Full matrix ― save ― name.
Method 2: Directly click on the second small icon matrix spreadsheet (create the UCINET matrix data table) in the shortcut bar in the software-paste the original data-click save to save the data-name.
The second step: 2-mode to 1-mode.
Reason: In the original data, we built a matrix containing the author and keywords of the document. If there is a keyword in the author’s document, then we will be at the intersection of the author’s row and the column of the keyword. Fill in 1. However, if we want to study a research hotspot in a certain field, we need to establish a "keyword"-"keyword" 1-mode network.
Operation steps: Data―Affiliations (2-mode to 1-mode)―Input dataset (select the DL format file with the suffix ".##h" in the previous step)―Column―OK.
Step 3: Generate numerical centrality.
Operation steps: Network―centrality―Degree―Inputdataset (select the file with the suffix "Aff.##h" saved in the previous step)―OK.
Step 4: Draw the center degree graph.
Operation steps: Visualize―NetDraw―file―open―ucinetdataset―Network―".##h"―OK.
Step 5: Draw the final center degree graph.
Operation steps: Analysis―centralitymeasures―Set Node Sizes by―Degree.
Step 6: Analyze the results.
From the graph generated in the fifth step, we can see that the icon area of the nodes "emergency capability" and "corporate reputation" is the largest, which indicates that the centrality of these two nodes is higher than that of other nodes, and accordingly, "emergency capability" And "corporate reputation" are also popular keywords in papers in this field.
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咱们下周见！
参考资料：
<1>https://baike.so.com/doc/5119081-5348155.html.
<2>周勤师姐的学习文档.
英文翻译：Google翻译。
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