# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 3 Methodology
### CiteSpace Tool for Scientometric Analysis  
In this study, we selected the CiteSpace software of version 6.2.R4 to conduct the scientometric analysis of the documents obtained in Section 3.1, since it is an efficient tool  
Figure 4: Statistics on the number of papers published each year. Figure 3: An overall framework for conducting scientometric survey in the event knowledge graph field. developed by Java language that enables research network analysis and visualization [36]. A variety of networks can be built and analyzed ranging from co-occurrence networks to co-citation networks, including author co-citation networks, journal co-citation networks, collaborative country networks, and keyword co-occurrence networks in this work. The co-citation networks refer to the fact that two documents have a co-citation relationship, i.e., they appear in the bibliography of another document. In this work, we conducted analysis of both the author co-citation network and the journal co-citation network. In these two networks, the nodes are set as cited authors and journals, respectively. The co-citation relationship between them is represented by the links between the nodes. Through constructing and analyzing those networks in CiteSpace, we can identify the representative nodes of great significance in these networks, e.g., core authors and influential journals. The co-occurrence networks refer to the fact that collaboration exists if different countries or keywords are present in a paper at the same time. Each network consists of nodes and links. In a co-occurrence network generated through CiteSpace, the nodes are set to collaborating countries and keywords, respectively. The node size indicates the frequency with which an author, an institution, or a country publishes a paper. The interaction between them is represented by the link between a pair of nodes. The strength of their collaborative relationship is represented by the thickness of the link. In CiteSpace, the number of co-occurrences is a parameter for calculating the strength of collaborative relationships. Based on the help document of CiteSpace, the strength is computed by the cosine method, i.e.,:  
\[\text{Cosine}\big{(}c_{ij},s_{i},s_{j}\big{)}=\frac{c_{ij}}{\sqrt{s_{i}s_{j}}}\]  
where \(s_{i}\) is the frequency of node \(i\), \(s_{j}\) is the frequency of \(j\), and \(c_{ij}\) is the number of co-occurrences of \(s_{i}\) and \(s_{j}\). The cosine value is between 0 and 1, where the higher the cosine value, the higher the strength of a collaborative relationship. In this paper, we study and analyze collaborative country networks and keyword co-occurrence networks. With regard to the visualization of the co-citation and co-occurrence networks, they are both color-based. The color of a link reflects the time slice when the co-occurrence or co-citation relationship was first created. The earliest years are in grey and purple, the middle ones are in green, and the most recent years are in orange and red. Among all those aforementioned networks, betweenness centrality, citation frequency, and citation burst are usually used as effective metrics to quantitatively interpret the networks. The betweenness centrality of a node is the proportion of all geodesic lines (shortest paths) between pairs of other vertices including that vertex [37], the score of which falls between 0 and 1. The higher the betweenness centrality score, the greater the importance and influence of that node. To discriminate, we use circles to highlight the nodes with high betweenness centrality. The greater the thickness of the circle, the higher the betweenness centrality score. The citation frequency refers to the times of a node being cited in a certain period of time, based on which we can understand the influence of a node and its popularity in the event knowledge graph field. The greater the citation frequency of the node citations, the larger the node size, indicating that the node (e.g., author and journal) has received significant attention in a given time period.