The rise of data-driven decision-making has placed immense importance on capturing and understanding the intricate relationships within complex systems. This is where graphs emerge as a powerful tool, offering a unique and highly effective way to represent data (Divecha CA et al., Journal of Postgraduate Medicine 69:125-131, 2023, Slutsky DJ, Journal of Wrist Surgery 3:67–8, 2014). Their ability to capture not just individual data points but also the connections and interactions between them unlocks a wealth of opportunities for analysis and exploration (Shuman DI et al., IEEE Signal Processing Magazine 30:83, 2013). Graph representation offers a clear and insightful lens through which to view complex datasets. Unlike traditional data structures that struggle to handle interconnectedness, graphs inherently capture the intricate relationships between entities (Shuman DI et al., IEEE Signal Processing Magazine 30:83, 2013). This enables analysts to identify patterns, trends, and hidden connections that would otherwise remain invisible. Imagine analysing the flow of information within a social network, the interconnectedness of proteins in a biological system, or the relationships between financial institutions in a global market. All these scenarios become readily accessible through the power of graphs (Barabasi AL, Network science. Cambridge University Press, 2016). The benefits of graph representation extend beyond mere visualization. It empowers the development of sophisticated algorithms like graph neural networks (GNNs).