  Darknet market forums are frequently used to exchange illegal goods and
services between parties who use encryption to conceal their identities. The
Tor network is used to host these markets, which guarantees additional
anonymization from IP and location tracking, making it challenging to link
across malicious users using multiple accounts (sybils). Additionally, users
migrate to new forums when one is closed, making it difficult to link users
across multiple forums. We develop a novel stylometry-based multitask learning
approach for natural language and interaction modeling using graph embeddings
to construct low-dimensional representations of short episodes of user activity
for authorship attribution. We provide a comprehensive evaluation of our
methods across four different darknet forums demonstrating its efficacy over
the state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2X
on Recall@10.
