Hypergraph Contrastive Learning for Drug Trafficking Community Detection

In recent decades, due to the lucrative profits, the crime of drug trafficking has evolved with modern technologies. Social media, as one of the popular online platforms, have become direct-to-consumer intermediaries for illicit drug trafficking communities to promote and trade drugs. These group-wise drug trafficking activities pose significant challenges to public health and safety, requiring urgent measures to address this issue. However, existing works against the imminent problem still face limitations, such as primarily analyzing individual roles from a single perspective, ignoring the group-wise relationships, and requiring sufficient labeled samples for model training. To this end, we propose a novel HyperGraph Contrastive Learning framework called HyGCL-DC that employs hypergraph to model the higher-order relationships among users to detect Drug trafficking Communities. Firstly, we build a hypergraph called Twitter-HyDrug including online user nodes and four types of hyperedges to depict the rich group-wise relationships among these users. Then, we leverage hypergraph neural networks to model the rich relationships among nodes and hyperedges in the drug trafficking hypergraph. Furthermore, we design a hypergraph self-supervised contrast module, which integrates the augmentation from the structure view and the attribute view to enhance hypergraph representation learning over unlabeled data. Finally, we design an end-to-end framework that combines the self-supervised contrastive module and the supervised module to classify online drug trafficking communities. To comprehensively study the online drug trafficking problem and evaluate our model, we conduct extensive experiments over Twitter-HyDrug and three citation benchmark hypergraph datasets to demonstrate the effectiveness of our model. Our new data and source code are available at https://github.com/HyGCL-DC.