# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 4 Methodology Proposal
### Stage 5: Graph Visualization
Some examples of display rules are: (a) appropriate establishing of color schemes and dimensions for nodes and its relationships, which can be useful in a heterogeneous graph, where nodes may be color-coded by type, such as products in blue, categories in yellow, and customers in green, enhancing visual distinction; (b) definition of constraints to determine the inclusion or exclusion of specific nodes and relationships relevant to a particular analysis, which can be useful to exclusively show products with sales greater than 200 units in a single day, limited to the seafood category; (c) clustering criteria for building communities among nodes or relationships that share distinctive attributes, identifying resulting structural features within the graph; for instance, one might needs to group all customers who purchased from the cereal category on Friday mornings, encouraging them to identify purchasing patterns; (d) display labels of nodes and relationships to show supplementary information of the graph; for example, using this rule, the display of product nodes could be enriched by displaying their attributes, such as the expiration date in addition to the description. The reader can refer to [61] for guidance in this task. (5.4) Highlight Structural and Attribute Patterns involves the capacity to recognize recurring patterns within a graph's structure and the attributes linked to its nodes and relationships. These patterns are essential for comprehending the nodes and relationshipsinherent in a graph, delivering insights into how its elements interrelate. Structural patterns concern the organization of nodes and relationships within the graph. They encompass the detection of subgraphs, cycles, hierarchies, clusters, or any other structural configurations in the graph. For instance, within an airport network, one might identify the airport or set of airports that serve as a pivotal hub, connecting numerous others and facilitating transit between multiple destinations. Attribute patterns center on the attributes or characteristics affiliated with the graph's nodes and relationships. These attributes can be instrumental in grouping nodes sharing common characteristics or purposes. For example, it might be advantageous to identify attributes that group airports commonly used to improve traffic congestion. The reader can refer to [62] for guidance in this task. (5.5) Use a Timeline concerns a visual representation of a data sequence that illustrates the progression or alterations within a graph over a specified period of time. This representation tracks and comprehends the developmental trajectory of nodes, relationships, and attributes associated with a graph. Timeline become particularly useful when graph data incorporates temporal dimensions. These dimensions are instrumental for monitoring and scrutinizing graph element fluctuations in relationships or characteristics across distinct time points. Moreover, it is indispensable for procuring dynamic insights and enhancing the grasp of the temporal dynamics inherent in data during graph analysis. For example, a timeline may reveal how connections between products and customers have transformed, identifying products that have experienced shifts in desirability throughout specific periods of the year. Another illustrative scenario involves exposing the propagation of a disease within a graph over days, weeks, or months. The reader can refer to [63] for guidance in this task. (5.6) Represent the Graph Results entails the presentation and visualization of outcomes following the analysis of a graph. This task involves transforming the gathered insights from the graph into a comprehensible and expressive format for users. Effectively communicating the findings and results from graph analysis ensures their comprehensibility and value to the user. The representation of these results can manifest in diverse formats, contingent upon the nature of the information and the analysis's objectives.