Visualizing Blockchain Transaction Behavioural Pattern: A Graph-based Approach

This paper proposes a novel graph-based visualisation approach to incorporating automated graph modelling and generalised graph algorithms from blockchain transactions. Our approach enables users to interact directly with the blockchain data using graph queries and provides exploration capabilities through graph patterns. Four case studies are presented, demonstrating the benefits of the proposed approach in identifying the behaviour of anomalous nodes. The visual assessment of behavioural patterns informs the challenges in classifying anomalous nodes. The proposed approach also has the potential to be used in other types of networks for inferring and verifying heuristics.