# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 5 Case Studies
### Product Recommendation
It consists of ten types of nodes and ten types of relationships. These nodes represent different entities within the graph, while the relationships denote their connections and interactions. This comprehensive model provides a structured representation of the Northwind dataset, enabling practical analysis and exploration of its interconnected elements. Figure 4: The subgraph consists of 830 orders (represented in blue), 77 products (represented in red), and 8 categories (represented in yellow), each with new relationships indicating products bought together and categories bought together. Figure 5: The top 5 selling categories have been identified and chosen inside the circles. If we exclusively opt for the category with an identifier equal to four, it displays the comprehensive details, including the total number of products, the identifier for each product, the total count of orders, and the identifier for each order. Figure 6: This subgraph consists of the 7 top-selling items, represented by red nodes, along with the 242 orders in which they appear, denoted by blue nodes. Additionally, the subgraph highlights the three categories to which these items belong, distinguished by yellow nodes. This visual representation clearly explains the relationships between the best-selling items, their order frequencies, and their corresponding categories. By examining this subgraph, valuable insights can be gained regarding the popularity and classification of these items within the dataset. In Stage 6, completion of all tasks is mandatory. The questions outlined in Stage 1 are the foundation for evaluating the results. The questions of this case were as follows: (1) What are the top 5 categories of products frequently purchased together? (2) Which products are among the top 5 most commonly purchased together? (3) Given a specific product, what are the provided purchase recommendations? (4) Should a product be unavailable, what is the suggested substitute for a recommendation? Based on the analysis, the top 5 categories frequently bought together are Dairy Products and Confections, Dairy Products and Beverages, Seafood and Beverages, Condiments and Beverages, and Confections and Beverages. The top five products often bought together are Sirop d'rable and Sir Rodney's Scones, Gorgonzola Telino and Pavlova, Camembert Pierrot and Pavlova, Flotemysost and Camembert Pierrot, and Mozzarella di Giovanni and Gorgonzola Telino. If we select Chang as a specific product, the recommended products to purchase together are Aniseed Syrup, [PERSON]'s Cajun Seasoning, Grandma's Boysenberry Spread, [PERSON]'s Organic Dried Pears, or Northwoods Cranberry Sauce. If we consider Mozzarella di Giovanni, the substitute products could be Camembert Pierrot, Flotemysost, Geitost, Gorgonzola Telino, Gudbrandsadsost, Mascarpone Fabioli, Queso Cabrales, Queso Manchego La Pastora, or Raclette Courdavault. The dashboard (Figure 7) can be utilized to select any other product and obtain recommendations or substitute products (task 6.1). If the information is updated, the recommended categories and products may vary based on customer orders. Since the analysis was performed using data extracted from a relational database within a specific timeframe, it is crucial to document the results associated with a given date. By documenting this information, we can conduct an analysis based on the timeline of product and product category behaviors (task 6.2). The following steps would be to create new questions based on the results (task 6.3). Because we follow the methodology, we have identified the key categories and products frequently bought together. Additionally, we have developed a dashboard that integrates the results and enables users to select specific products for purchase recommendations or substitute product suggestions. This approach significantly benefits understanding consumer behavior and enhancing the shopping experience.