Year: 2025
Type: conference-paper
Abstract: As the market for illicit drugs remains extremely profitable, major online platforms have become direct-to-consumer intermediaries for illicit drug trafficking participants.These online activities raise significant social concerns that require immediate action.Existing approaches to combating this challenge are generally impractical due to the imbalance of classes and scarcity of labeled samples in realworld applications.To this end, we propose a novel Large Language Model-empowered Heterogeneous Graph Prompt Learning framework for illicit Drug Trafficking detection, called LLM-HetGDT, that leverages LLM to facilitate heterogeneous graph neural networks (HGNNs) to effectively identify drug trafficking activities in the class-imbalanced scenarios.Specifically, we first pre-train HGNN over a contrastive pretext task to capture the inherent node and structure information over the unlabeled drug trafficking heterogeneous graph (HG).Afterward, we employ LLM to augment the HG by generating high-quality synthetic user nodes in minority classes.Then, we finetune the soft prompts on the augmented HG to capture the important information in the minority classes for the downstream drug trafficking detection task.To comprehensively study online illicit drug trafficking activities, we collect a new HG dataset over Twitter, called Twitter-HetDrug.Extensive experiments on this dataset demonstrate the effectiveness, efficiency, and applicability of LLM-HetGDT.Our source code and dataset are available here.
Authors: Tianyi Ma, Yiyue Qian, Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: III: Small: A New Machine Learning Paradigm Towards Effective yet Efficient Foundation Graph Learning Models, SCC-IRG Track 1: A New AI-driven Paradigm to Promote Community Resilience for Teenagers and Young Adults in Preventing Opioid Misuse and Addiction, CAREER: Taming Networks in the Wild: A Safety-Centric Network Learning Framework, III: Medium: A Data-driven and AI-augmented Framework for Collaborative Decision Making to Combat Infectious Disease Outbreaks, EAGER: A New Explainable Multi-objective Learning Framework for Personalized Dietary Recommendations against Opioid Misuse and Addiction, D-ISN: An AI-augmented Framework to Detect, Disrupt, and Dismantle Opioid Trafficking Networks
OpenAlex ID: W4412888279