# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 3 Methodology
### Scientometric and Visual Analysis Using CiteSpace
With regard to the networks generated by CiteSpace, they are composed of nodes and lines. The nodes refer to the objects to be analyzed (e.g., authors and documents), and the lines represent the interaction between nodes. The thicker the line is, the stronger the relationship between the nodes is. The size of nodes indicates the importance of the objects in the network, which can be measured by a variety of indicators, such as citation frequency and the number of publications. The concentric ring of different colors enclosing  
Figure 3: The distribution of the literature types for the years 2012–2022. Figure 2: The number of publications on ”event prediction” for the years 2012–2022. the nodes represents the time span information of the nodes. Each color ring represents a specific year. The networks can be analyzed and visualized based on degree centrality, betweenness centrality, closeness centrality, citation frequency, or citation burstiness. Centrality measures the importance of a node within a network in terms of its connections, interactions, and influence on other nodes. In this paper, degree centrality, betweenness centrality, and closeness centrality were used for centrality measurement. The degree of centrality quantifies the number of direct connections a node has in the network. Nodes with higher degrees are regarded as more central due to their larger number of immediate connections. The betweenness centrality is used to quantify the degree of mediation of nodes in the network, that is, the importance of nodes in the shortest path between different nodes. By calculating the betweenness centrality of nodes, we can identify the central nodes in the network. In CiteSpace, the betweenness centrality score ranges between 0 and 1. Nodes with a betweenness centrality that is not less than 0.1 are highlighted with purple concentric rings. The thickness of the purple rings is proportional to the betweenness centrality score. The closeness centrality is calculated by taking the reciprocal of the sum of the shortest path lengths from a node to all other nodes in the network. The higher the closeness centrality value, the more central the node is in terms of its proximity to others. Citation frequency reflects the number of times a node is cited within a specific time period, providing insights into the influence and popularity of that node in the field of event prediction. Nodes that experience a substantial increase in citations within a specific time frame are identified as \"burst\" nodes. The occurrence of citation bursts allows researchers to discern trends in research development. Furthermore, we performed cluster analysis for the author co-citation network, document co-citation network, and keyword co-occurrence network, where two metrics including modularity and silhouette value were used to explain the scholarly framework of the event prediction domain. Modularity is a metric that quantifies the extent to which a network can be partitioned into distinct components or modules. The modularity measures the degree of connection between nodes in the same module divided by the degree of connection between nodes in different modules, which is usually measured using the Q value. If a network has a high modularity (i.e., the Q value is larger than 0.3), it means that there is an obvious modular structure in the network. The silhouette value, also known as the S value, which ranges from \(-1\) to 1 [9], is another metric used to assess the quality of a clustering configuration. A higher silhouette score indicates a higher level of homogeneity within the cluster and a higher level of heterogeneity among clusters. The S value of 0.7 is often taken as the threshold to determine whether the clustering results are reasonable. With the purpose of interpreting the clustering results, we adopted the log-likelihood ratio (LLR) algorithm for label analysis methods, which generates a set of representative terms to explain the cluster content.