# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 5 Case Studies
### Path Recommendation
In Stage 3, the decision was made to employ path-finding algorithms to address the route that enables us to visit all the museums (task 3.1). Path-finding algorithms are particularly advantageous when the goal is to identify optimal routes, determine the shortest paths, or analyze flow patterns within the network. Consequently, centrality, community, or similarity algorithms are not applied in this context. Instead, the Minimum Spanning Tree algorithm (MST) is implemented to identify the path with the least total weight, facilitating the visitation of all museums (task 3.5). After the completion of Stage 3, a thorough evaluation of information adequacy precedes our progression to Stage 4. In Stage 4, we generate a subgraph to identify the points with the tourism attribute, whose value corresponds to a museum. The outcome yields 11 nodes and 121 relationships (Figure 13) (task 4.4). To determine the shortest path connecting all the museums, we modify the graph's topology by introducing relationships with the lowest weights among the museums (task 4.1). Additionally, we incorporate a relationship named MST resulting from applying the minimum spanning tree algorithm to identify the path with the least total weight that allows us to visit all the museums. These modifications in topology and the inclusion of a new structural attribute led to the creation of the subgraph depicted in Figure 13 (task 4.2). Upon completing Stage 4, we verify the adequacy of the information and proceed to stage 5. In Stage 5, we emphasize implementing a dashboard (Figure 14) to showcase the eleven museums and the Minimum Spanning Tree, enabling visitation to each (task 5.6). Upon the completion of Stage 5, a comprehensive verification of the information's adequacy precedes our transition to Stage 6. Figure 13: The subgraph comprises points registered in the graph database, specifically representing museums and their corresponding georeferenced distances. In Stage 6, completion of all tasks is mandatory. The initial question was looking for a network of connections among pre-registered tourist attractions to minimize the total cost while disregarding the direction of the connections. We achieved this goal by obtaining the minimum spanning tree that allows us to visit the following eleven museums: Museo de Ripley's Aunque used no lo crea!, Panteon de Belen, Museo Del Periodismo, Museo del Periodismo y Artes Grificas, Museo de la Ciudad, Museo de las Culturas Populares, Museo de Cera Guadalajara, Museo de Arte Sacco, Casa Museo Lopez Portillo, Ex Convention Del Carmen, and Museo de las Artes Populares de Jalisco. Thus, we fulfilled the requirement and answered the question (task 6.1). Given the dynamic nature of OSM, it is crucial to accurately document the findings with a specific date, considering the continuous addition of new tourist attractions. Since the analysis was based on an OSM extract from a specific period, it is essential to ensure that the results are associated with the corresponding date. By continuously monitoring and analyzing the behavior of the information, we can adapt and optimize the path recommendation system to accommodate future updates (task 6.2). In the following steps, we could expand the quadrant of the map or choose another city (task 6.3). In conclusion, implementing the proposed methodology has yielded significant results in establishing a network of connections among pre-registered tourist attractions, resulting in minimized costs when visiting the eleven museums. The analysis performed on the OSM extract has provided valuable insights into the connectivity patterns of these attractions. The use of this methodology in the tourism domain shows its potential to enhance the visitor experience and optimize travel routes in various regions.