# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 5 Case Studies
### Airports  
Airports are a critical component of global connectivity, and identifying the most important airports within their network of connections is crucial. This case study aims to determine these key airports and recommend investment in their improvement or expansion. Initially, the information is stored in two comma-separated files. We follow the KDG methodology to analyze. Specifically, we use tasks 1.5, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 4.1, 4.2, 4.4, 5.6, 6.1, 6.2, and 6.3. According to the methodology, we should check all tasks of stage 1. However, as in case 1, there are no defined business goals, information requirements, metrics, or project plans. Although tasks 1.1 to 1.4 are missing, the methodology indicates that we must apply at least task 1.5, in which we must define at least one question for decision making that guides the analysis to meet the initial request. In this scenario, we formulate the following decision-making question: What are the most important airports in our network of connections? To refine our response, we establish three subquestions: (1) Which five airports have the highest number of immediate connections with other airports? (2) Which five airports could play a pivotal role in maintaining subsequent connections due to their extensive network ties? (3) What are the top five airports ideally suited for stopovers? (task 1.5). Following the methodology, we advance to stage 2, where completing all tasks is obligatory. The primary task (2.1) entails conducting an exploratory information analysis. The initial CSV file contains airport details such as name, country, latitude, longitude, and a unique identifier, while the second file provides information on airport connections. Notably, all connections are bidirectional, representing both departure and arrival routes. The files include 225 countries with at least one registered airport. In order to model the graph, we will represent each airport as a node connected to itself using the \"Connected-to\" relationship, as illustrated in Figure 8 (task 2.2). Progressing to task (2.3), we opt for NEO4J as the data storage solution because it is a graph-based database. Subsequently, we craft a Cypher script to extract information from the CSV files, transforming airport details into nodes with attributes and connections into edges. Lastly, we populate a new database instance with the processed data (task 2.4). With the completion of all tasks in stage 2, we verify the sufficiency of the information and progress to stage 3. In Stage 3, we decided to utilize centrality algorithms for addressing the subquestions related to airports with the most immediate connections, those crucial for continuity, and the ideal stopover airports (task 3.1). Centrality algorithms prove valuable in identifying critical nodes within the network, making them well-suited for our objectives at this stage. We specifically apply them to airport nodes (task 3.2), excluding the application of community, similarity, or pathfinding algorithms (tasks 3.2 to 3.4). Following the completion of Stage 3, we assess the sufficiency of the information before advancing to Stage 4. In Stage 4, there is no requirement to generate a subgraph for addressing the questions, as we exclusively have nodes of type airports and edges of type connected to, as shown in Figure 8 (task 4.4). We modify the topology and include the attributes of degree, betweenness centrality, and closeness centrality which will be added to the airport nodes. The degree attribute will help answer subquestion 1, while betweenness centrality will provide insights for subquestion 2. The closeness centrality attribute will address subquestion 3 (tasks 4.1 and 4.2). Ultimately, upon applying summarization to both nodes and edges, the outcome is illustrated in Figure 8. Upon completing Stage 4, we verify the adequacy of the information and proceed to stage 5. In Stage 5, our focus shifts to implementing a dashboard (task 5.6), drawing insights from the tasks accomplished in Stage 4.