Graph mining has emerged as a significant field of research with applications spanning multiple domains, including marketing, corruption analysis, business, and politics. The exploration of knowledge within graphs has garnered considerable attention due to the exponential growth of graph-modeled data and its potential in applications where data relationships are a crucial component, and potentially being even more important than the data themselves. However, the increasing use of graphs for data storing and modeling presents unique challenges that have prompted advancements in graph mining algorithms, data modeling and storage, query languages for graph databases, and data visualization techniques. Despite there being various methodologies for data analysis, they predominantly focus on structured data and may not be optimally suited for highly connected data. Accordingly, this work introduces a novel methodology specifically tailored for knowledge discovery in labeled and heterogeneous graphs (KDG), and it presents three case studies demonstrating its successful application in addressing various challenges across different application domains.

Knowledge Graphs (KGs) have emerged as a powerful tool for representing semantic structured information and enabling the development of intelligent systems. This paper focuses on the generation of semantic maps as summarization method for KGs. We propose a strategy that utilizes centroid-based clustering algorithms, namely Affinity Propagation and Partitioning Around Medoids (PAM), to capture the semantic distance between nodes in the KG and generate meaningful clusters. Our experiments demonstrate divergent results between the two clustering algorithms, with Affinity Propagation showing qualitative coherence and meaningfulness, while PAM performs well in terms of internal validation metrics. We leverage the computed centroids to infer a main term of the semantic map, which contributes to the visually appealing and informative representation of the KG. The combination of semantic distance capture, clustering algorithms, and centroid-based inference facilitates a comprehensive understanding of the KG. Our findings highlight the importance of considering both qualitative and quantitative evaluation measures in assessing clustering results. The effectiveness of semantic maps is showcased in visualizing KGs and advancing the field of knowledge graph visualization. The integration of centroid-based clustering algorithms, qualitative evaluation, and inference methods offers improved clarity and interpretability for complex KG analysis.

In modern processor development, extensive simulation is required before manufacturing to ensure that Central Processing Unit (CPU) designs function correctly and efficiently. This pre-silicon validation process involves running a wide range of software workloads on architectural models to identify potential issues early in the design cycle. Improving pre-silicon simulation time is critical for accelerating CPU development and reducing time-to-market for high-quality processors. This study addresses the computational challenges of validating full-system simulations by leveraging unsupervised machine learning to optimize test case selection. By identifying patterns in executed instructions, the approach reduces the need for exhaustive simulations while maintaining rigorous validation standards. Notably, the optimized subset of test cases reduced simulation time by a factor of 10 and captured 97.5% of the maximum instruction entropy, ensuring nearly the same diversity in instruction coverage as the full workload set. The combination of Principal Component Analysis (PCA) and clustering algorithms effectively distinguished compute-bound and memory-bound workloads without requiring prior knowledge of the code. Statistical Model Checking with entropy-based analysis confirmed the effectiveness of this subset. This methodology significantly reduces validation effort, expedites CPU design cycles, and improves hardware efficiency. The findings highlight the potential of machine learning-driven validation strategies to enhance pre-silicon testing, enabling faster innovation and more robust processor architectures.