# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 3 Methodology
### CiteSpace Tool for Scientometric Analysis
The citation in a particular time slice is represented by a single-color ring. Thus, the temporal citation patterns of a node are indicated by the concentric rings around it. The citation burst is defined as the burstiness (i.e., sudden increase) of the nodes with regard to its citation frequency over time [38; 39]. If the number of citations of a node increases significantly in a certain period of time, it is marked as a \"burst\" node. In Citespace, the citation burst is detected based on the method proposed by [PERSON] in 2002 [39], which introduces the parameter \(\gamma\) ranging from 0 to 1 for burst detection. As \(\gamma\) is close to 0, it pays more attention to those nodes during short time intervals. While \(\gamma\) is close to 1, it considers all nodes more balanced, including those with longer time intervals. In this work, the \(\gamma\) was set as 1 in order to take all nodes into account for citation burst detection. The citation burstiness can help researchers grasp the research development trends. In addition, we conducted cluster analysis for the author co-citation network and journal co-citation network to clarify the domain scope as well as the research hotspots and trends. In CiteSpace, the clustering analysis mainly adopts the spectral clustering method, which divides clusters into a number of groups based on co-occurrence relationships. Specifically, an affinity matrix describing the similarity of pairs of data points based on a given sample dataset is first defined, following which the eigenvectors of the matrix are calculated, and then different data points are clustered based on the eigenvectors. In this study, the generated clusters were labeled with a set of representative terms for interpretation, which were extracted from the noun phrases (e.g., titles, abstracts, and keywords) in the documents based on the log-likelihood ratio (LLR) algorithm that is provided by CiteSpace. With regard to cluster \(C_{j}\), the feature vector \(V_{ij}\) is composed of the word \(w_{i}\) frequency (\(\alpha\)), concentration (\(\beta\)), and dispersion (\(\gamma\)). The label of the cluster is generated by calculating the LLR of each word based on the following equation. \[\text{LLR}=\log\frac{p\left(C_{j}\backslash V_{ij}\right)}{p\left(\overline{C_{ j}}\backslash V_{ij}\right)},\]  
where \(p\left(C_{j}\backslash V_{ij}\right)\) and \(p\left(\overline{C_{j}}\backslash V_{ij}\right)\) are the density function of \(V_{ij}\) in the cluster \(C_{j}\) and \(\overline{C_{j}}\), respectively. Those words with high LLR are the selected labels of the cluster. The modularity (i.e., Q value) and silhouette (i.e., S value) are used as key indicators to help us understand the structural properties of the academic networks in the event knowledge graph field. The modularity of a network measures the extent to which a network can be decomposed into multiple components or modules. This metric provides a reference for the overall clarity of a given decomposition of the network [38]. A high Q value indicates that the nodes in the network are tightly connected within clusters and are sparsely connected with other clusters, namely the network has a pronounced grouping structure. The silhouette of a cluster measures the quality of a clustering configuration, and the S value ranges from \(-1\) to 1 [38]. The higher the S value, the higher the homogeneity of the nodes within the cluster. A Q value over 0.3 and an S value over 0.7 are often desirable for network cluster analysis, meaning that the community structure of the network is significant and the clustering results have high confidence [40].