Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
Dec 6, 2021
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Yiyue Qian
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Yiming Zhang
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Yanfang Ye
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Chuxu Zhang
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About
Driven by the considerable profits, the crime of drug trafficking (a.k.a. illicit drug trading) has co-evolved with modern technologies, e.g., social media such as Instagram has become important platform for marketing and selling illicit drugs. The activities of online drug trafficking are nimble and resilient, which call for novel techniques to effectively detect, disrupt, and dismantle illicit drug trades. In this paper, we propose a holistic framework named MetaHG to automatically detect illicit drug traffickers on social media (i.e., Instagram), by tackling following two new challenges: (1) different from existing works which merely focus on analyzing post content, MetaHG is capable of jointly modeling post content and relational structured information on social media for illicit drug trafficker detection; (2) in addition, through the proposed meta-learning technique, MetaHG addresses the issue of requiring sufficient data for model training. More specifically, in our proposed MetaHG, we first build a heterogeneous graph (HG) to comprehensively characterize the complex ecosystem of drug trafficking on social media. Then, we employ relation-based graph convolutional neural network to learn node (i.e., user) representations over the built HG, in which we introduce graph structure learning to compensate sparse relational information among entities in the HG for more robust node representation learning. Afterwards, we propose a meta-learning algorithm to optimize model parameters. A self-supervised module and a knowledge distillation module are further designed to exploit unlabeled data for improving model. Extensive experiments based on the real-world data collected from Instagram demonstrate that the proposed MetaHG outperforms state-of-the-art methods.Driven by the considerable profits, the crime of drug trafficking (a.k.a. illicit drug trading) has co-evolved with modern technologies, e.g., social media such as Instagram has become important platform for marketing and selling illicit drugs. The activities of online drug trafficking are nimble and resilient, which call for novel techniques to effectively detect, disrupt, and dismantle illicit drug trades. In this paper, we propose a holistic framework named MetaHG to automatically detect illi…
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NeurIPS 2021
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About NeurIPS 2021
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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