Transaction graph analysis of blockchain networks is a pivotal area of research, providing insights into transaction flows, user behavior, and potential illicit activities. By modeling blockchain transactions as graphs, researchers can trace the movement of assets, identify clusters of related addresses, and detect patterns indicative of fraud or money laundering. Techniques such as personalized PageRank algorithms on directed, weighted, and temporal transaction graphs allow efficient tracing of transactions in account-based blockchains. Additionally, analyzing transaction subgraphs helps identify malicious accounts by capturing topological patterns. Large-scale graph datasets enable comprehensive study of blockchain networks, facilitating a deeper understanding of user behavior, anomaly detection, and overall enhancement of transparency and security within blockchain ecosystems.