<p>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.</p>

<p>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.</p>

<p>The study analysed the importance of blockchain transaction features to identify suspicious activities. The feature engineering process involves exploiting domain knowledge, applying intuition, and performing a time-consuming series of trial-and-error extractions. Manually overseeing this process significantly impacts the performance of model generation. We address this challenge with an automated feature engineering approach to extract the various features from blockchain transactions. Also, we engineered a set of new features based on statistical measures and graph representation. We demonstrate that the proposed approach can be applied to various blockchain transaction datasets, including Bitcoin and Ethereum. The engineered features were tested against eight classifiers, including random forest, XG-boost, Silas, and neural network-based classifiers to identify the suspicious behaviour of transactions</p>

<p>The study analysed the importance of blockchain transaction features to identify suspicious activities. The feature engineering process involves exploiting domain knowledge, applying intuition, and performing a time-consuming series of trial-and-error extractions. Manually overseeing this process significantly impacts the performance of model generation. We address this challenge with an automated feature engineering approach to extract the various features from blockchain transactions. Also, we engineered a set of new features based on statistical measures and graph representation. We demonstrate that the proposed approach can be applied to various blockchain transaction datasets, including Bitcoin and Ethereum. The engineered features were tested against eight classifiers, including random forest, XG-boost, Silas, and neural network-based classifiers to identify the suspicious behaviour of transactions</p>