The European Natural Gas Network can be turned into a knowledge graph using Neo4j to analyze its data. The network consists of various components such as pipelines, compressors, LNGs, storages, consumers, and power plants. Each component has a unique structure with specific attributes like node_id, lat, long, country_code, etc. The knowledge graph is created by defining the constraints and then creating the components. Exploratory data analysis (EDA) and graph data science (GDS) are used to analyze the graph. GDS algorithms such as PageRank, Degree Centrality, Betweenness Centrality, and Cluster Detection Via Louvain Modularity are applied to find important nodes and communities in the network. PathFinding is also used to determine the shortest path between two nodes. Finally, NeoDash is used to visualize the graph and create interactive dashboards.