Systems and methods for combining graph embedding and random forest classification for improving classification of distributed ledger activities

Abstract

A method for combining an embedding of a graph having a plurality of nodes and edge connections and classifications of distributed ledger-based activities may include: receiving information for historical distributed ledger-based activities comprising identifications of the plurality of nodes in the graph as involved in illicit activities, licit activities, or unknown; applying sampling to sample labels and associated features; training a first classifier based on the plurality of historical distributed ledger-based activities, the node information, and the sample labels; receiving a current distributed ledger-based activity from the distributed ledger network; predicting a classification for the current distributed ledger-based activity using the first classifier; extracting features from the current distributed ledger-based activity; enriching the extracted features based on one or more neighbors of the nodes involved in the current distributed ledger-based activity; reclassifying the current distributed ledger-based activity using a second classifier; and outputting an alert based on the prediction.