The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets.Consequently, illegal markets and their connections are challenging to uncover on the Darknet.To identify relationships between illegal markets and their vendors, we propose Ven-dorLink, an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements (ads) on seven public Darknet markets.In contrast to existing literature, VendorLink utilizes the strength of supervised pre-training to perform closed-set vendor verification, openset vendor identification, and low-resource market adaption tasks.Through VendorLink, we uncover (i) 15 migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17 migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii) 75 migrants and 10 potential aliases in the Traderoute-Agora dataset.Altogether, our approach can help Law Enforcement Agencies (LEA) make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets.

Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics.Prior work relies on discrete citation relations to generate contrast samples.However, discrete citations enforce a hard cutoff to similarity.This is counter-intuitive to similarity-based learning and ignores that scientific papers can be very similar despite lacking a direct citation -a core problem of finding related research.Instead, we use controlled nearest neighbor sampling over citation graph embeddings for contrastive learning.This control allows us to learn continuous similarity, to sample hard-to-learn negatives and positives, and also to avoid collisions between negative and positive samples by controlling the sampling margin between them.The resulting method SciNCL outperforms the state-of-theart on the SciDocs benchmark.Furthermore, we demonstrate that it can train (or tune) language models sample-efficiently and that it can be combined with recent training-efficient methods.Perhaps surprisingly, even training a general-domain language model this way outperforms baselines pretrained in-domain.

While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-)supervised training. Thus, for TX-Ray, we modify the established computer vision explainability principle of ‘visualizing preferred inputs of neurons’ to make it usable for both NLP and for transfer analysis. This allows one to analyze, track and quantify how self- or supervised NLP models first build knowledge abstractions in pretraining (1), andthen transfer abstractions to a new domain (2), or adapt them during supervised finetuning (3) – see Fig. 1. TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. We find that, similar to Lottery Ticket based pruning, TX-Ray based pruning can improve test set generalization and that it can reveal how early stages of self-supervision automatically learn linguistic abstractions like parts-of-speech.