# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 6 Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**KDG Stages** & **KDG Tasks** & **CRISP DM [7]** & **DST [14]** & **KDD [6]** \\ \hline  & 1.1 Identify business goals & Business Understanding & Goal Exploration & Developing an understanding of the application domain and the relevant prior knowledge, and identifying the goal of the KDD process from the customer’s viewpoint. \\
1. Understanding the Analytical Process & 1.2 Identify information requirements & Business Understanding & Business Understanding & - \\  & 1.3 Identify successful metrics & Business Understanding & Business Understanding & - \\  & 1.4 Identify the project plan & Business Understanding & Business Understanding & Business Understanding & - \\  & 1.5 Define questions for decision making & Business Understanding & Data value exploration & - \\ \hline  & 2.1 Exploratory data analysis & Data Understanding & Data source exploration & Creating a target dataset \\  & 2.2 Design the graph model & - & - & - & - \\
2. Graph Building & 2.3 Select the data storage format & Data Preparation & Data Preparation & Creating a target dataset \\  & 2.4 Extract, transform, and load data & Data Preparation & Data Preparation & Data cleaning and preprocessing. Data reduction and projection \\ \hline  & 3.1 Select graph mining algorithms & Modeling * & Modeling * & Matching the goals of tile KDD process to particular data mining method. Choosing the data mining algorithms. \\
3. Graph Mining & 3.2 Apply centrality algorithms & - & - & - \\  & 3.3 Apply community algorithms & - & - & - \\  & 3.4 Apply similarity algorithms & - & - & - \\  & 3.5 Apply pathfinding algorithms & - & - & - \\ \hline  & 4.1 Modify topology & - & - & - \\
4. Graph & 4.2 Include structural attributes & - & - & - \\ Transformation & 4.3 Apply summarization & - & - & - \\  & 4.4 Generate subgraphs & - & - & - \\ \hline  & 5.1 Apply a layout & - & - & - \\  & 5.2 Perform drill-down or roll-up operations & - & - & - \\
5. Graph & 5.3 Define graph display rules & - & - & - \\ Visualization & 5.4 Highlight structural and attribute patterns & - & - & - \\  & 5.5 Use the timeline & - & - & - \\  & 5.6 Represent the graph results & - & Narrative exploration & Interpreting mined patterns \\ \hline  & 6.1 Evaluate results & Evaluation & Result exploration & Consolidating discovered knowledge \\
6. Evaluation & 6.2 Documentation of results & Evaluation & Product exploration & Consolidating discovered knowledge \\  & 6.3 Determine the next steps & Evaluation & Evaluation & - \\ \hline \hline \end{tabular}  
* Indicates the steps are partially similar to the KDG’s task, since other methodologies are not specific to graphs.  
\end{table} 
Table 1: Mapping the KDG’s tasks to CRISP-DM, DST, and KDD steps. is well-suited for analyzing highly interconnected information. However, if the analysis involves other types of information, this methodology may not be the most suitable choice. The user should understand the capabilities inherent in working with graphs. While the user will be provided with a guide to conduct the analysis, foundational knowledge of techniques, such as graph mining algorithms, is essential. The user must not be an expert but familiar with the subject. As part of our future work, we plan to integrate Machine Learning Operations (MLOps) and DevOps practices into our methodology to enhance its effectiveness and efficiency. By incorporating MLOps principles, we aim to streamline machine learning models' development, deployment, and management, ensuring smooth and scalable operations. Furthermore, we intend to validate the robustness of our methodology by applying it to a broader range of case studies, enabling us to gather more insights and validate them in different business scenarios. Conceptualization, V.H.O.-G., L.G.-P., F.C. and M.A.-M.; Methodology, V.H.O.-G., L.G.-P. and F.C. ; Software, V.H.O.-G.; Validation, V.H.O.-G., L.G.-P., F.C. and M.A.-M.; Formal analysis, L.G.-P.; Investigation, L.G.-P. and F.C.