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Abstract While economic sociology research and theory argue that excessive network embeddedness depresses competition in illegal markets, prior research does not examine how distinct types of embeddedness may have asymmetric effects on the diversity of purchasing behavior—the range of illegal goods that buyers typically purchase. This study considers how network embeddedness can positively or negatively affect drug purchasing diversity in online drug markets by referring buyers to new vendors or “locking” buyers into recurrent trade for the same products. We analyze novel network data on 16,847 illegal drug exchanges between 7205 actors on one online illegal drug market. Consistent with hypothesized network asymmetry, buyers are more likely to purchase a new type of drug when the transaction is part of an indirect network referral. Although histories of exchange increase the overall frequency of drug purchasing, they are associated with decreases in new drug-type purchases. In the aggregate, these processes either contribute to an integrated market where buyers purchase multiple drugs from multiple vendors (in the case of referrals) or a fragmented market characterized by recurrent trade from the same vendors for the same substances (in the case of repeated trade). We discuss the implications of these findings for research on embeddedness, illegal markets, risky exchange, and drug policy. 

Cryptomarkets—online markets for illegal goods—have revolutionized the illegal drug trade, constituting about 10% of all drug trades and attracting users to a greater variety and more addictive substances than available in offline drug markets. This review introduces the burgeoning area of sociology research on illegal cryptomarkets, particularly in the realm of drug trade. We emphasize the expanding role of illicit online trade and its relevance for understanding broader exchange challenges encountered in all illegal trade settings. Examining the effects of online illegal trade on consumption and supply-side policing, we also discuss the harm and potential benefits of moving drug exchange from offline to online markets. We argue for a network perspective's efficacy in this research domain, emphasizing its relevance in assessing trade and discussion networks, technical innovation, and market evolution and vulnerabilities. Concluding, we outline future research areas, including market culture, failure, and the impact of online illegal trade on stratification. 

Abstract Although economic sociology emphasizes the role of social networks for shaping economic action, little research has examined how network governance structures affect prices in the unregulated and high-risk social context of online criminal trade. We consider how overembeddedness—a state of excessive interconnectedness among market actors—arises from endogenous trade relations to shape prices in illegal online markets with aggregate consequences for short-term gross illegal revenue. Drawing on transaction-level data on 16 847 illegal drug transactions over 14 months of trade in a ‘darknet’ drug market, we assess how repeated exchanges and closure in buyer–vendor trade networks nonlinearly influence prices and short-term gross revenue from illegal drug trade. Using a series of panel models, we find that increases in closure and repeated exchange raise prices until a threshold is reached upon which prices and gross monthly revenue begin to decline as networks become overembedded. Findings provide insight into the network determinants of prices and gross monthly revenue in illegal online drug trade and illustrate how network structure shapes prices in criminal markets, even in anonymous trade environments. 

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 further increasing the difficulty of linking users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and model interactions 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.