This project investigates graph learning methods for large, multilayer cryptoasset transaction graphs. The expected results and innovations are: (i) a collaboratively constructed cryptoasset knowledge graph that serves as a well-defined ground truth dataset for learning tasks; (ii) novel, systematically evaluated graph learning methods tailored to cryptoasset analytics task; and (iii) a novel multilayer conceptualization for transaction graphs and a systematic evaluation of scalable graph processing approaches.