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In this work we focus on identifying key players in dark net cryptomarketsthat facilitate online trade of illegal goods. Law enforcement aims to disruptcriminal activity conducted through these markets by targeting key playersvital to the market's existence and success. We particularly focus on detectingsuccessful vendors responsible for the majority of illegal trade. Ourmethodology aims to uncover whether the task of key player identificationshould center around plainly measuring user and forum activity, or that itrequires leveraging specific patterns of user communication. We focus on alarge-scale dataset from the Evolution cryptomarket, which we model as anevolving communication network. While user and forum activity measures areuseful for identifying the most successful vendors, we find that betweennesscentrality additionally identifies those with lesser activity in the network.But more importantly, analyzing the forum data over time, we find evidence thatattaining a high betweenness score comes before vendor success. This suggeststhat the proposed network-driven approach of modelling user communication mightprove useful as an early warning signal for key player identification.

Early warning signals for predicting cryptomarket vendor success using dark net forum networks