Design Thinking-based Graph Mining Technique for Discover Patterns in Complex Networks

Complex networks undergird everything from social media interactions to biological processes; therefore, uncovering their concealed patterns is more crucial than ever in today's digital world. The proposed work investigates graph mining techniques, emphasizing Frequent Subgraph Mining, to identify recurring themes and relationships in complex networks. The proposed study investigates in depth the relationship between graph representation, node and edge attributes, and network metrics, enabling analysts to evaluate a wide variety of data easily. Examining the applicability of Frequent Subgraph Mining in numerous contexts, ranging from social networks to biological systems, facilitates decision-making and inspires originality. The results demonstrate that these techniques are useful for identifying patterns and are computationally efficient, making them indispensable instruments for understanding the digital foundations of complex networks.