Abstract: Disclosed are methods, systems, and other implementations, including a method for identifying and predicting illegal digital currency transactions that includes obtaining one or more blockchains of transaction blocks for transactions involving digital currency, deriving from the one or more blockchains of transaction blocks a transaction graph of sequential transactions, and applying clustering processing to the transaction graph to generate resultant one or more entity graphs representative of likely chains of digital currency transfers by respective one or more entities. The method further includes extracting graph feature data from the resultant one or more entity graphs, and applying classification processing (e.g., supervised learning classification processing) to the extracted graph feature data to identify a suspected malicious entity from the one or more entities associated with the one or more entity graphs.