Open data makes it possible to gain insights into the transaction patterns of blockchain projects. These patterns can be modeled as transaction networks, which support a wide range of analytical techniques. Depending on the trade-off between information preservation and complexity reduction, various graph representations can be used to capture additional features, temporal changes, and interoperability between protocols. Different analytical approaches, including calculating graph metrics or applying graph neural networks, can reveal hidden structures, uncover unusual activities, detect anomalies, and provide a clearer picture of the dynamics of blockchain projects. While network science metrics and machine learning methods have been extensively applied to transaction networks, graph combinatorial optimization problems remain largely underexplored in this domain, despite their potential to identify critical nodes, hidden substructures, and flow patterns. The goal of this paper is to assess the applicability of graph combinatorial optimization problems to blockchain transaction networks, systematically review existing analytics approaches, discuss their respective strengths and limitations, and explore how combining different techniques can yield deeper insights into the structural and functional properties of blockchain ecosystems.
Palk et al. (2026) studied this question.