Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911826890981376 |
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| author | Xu, Zequan Sun, Qihang Hu, Shaofeng Shi, Jieming Li, Hui |
| author_facet | Xu, Zequan Sun, Qihang Hu, Shaofeng Shi, Jieming Li, Hui |
| contents | The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers. In this paper, we propose a novel contrastive multi-view learning method named CMT for crowdsourcing fraud detection over the heterogeneous temporal graph (HTG) of MMMA. CMT captures both heterogeneity and dynamics of HTG and generates high-quality representations for crowdsourcing fraud detection in a self-supervised manner. We deploy CMT to detect crowdsourcing frauds on an industry-size HTG of a representative MMMA WeChat and it significantly outperforms other methods. CMT also shows promising results for fraud detection on a large-scale public financial HTG, indicating that it can be applied in other graph anomaly detection tasks. We provide our implementation at https://github.com/KDEGroup/CMT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_02793 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph Xu, Zequan Sun, Qihang Hu, Shaofeng Shi, Jieming Li, Hui Social and Information Networks Artificial Intelligence The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers. In this paper, we propose a novel contrastive multi-view learning method named CMT for crowdsourcing fraud detection over the heterogeneous temporal graph (HTG) of MMMA. CMT captures both heterogeneity and dynamics of HTG and generates high-quality representations for crowdsourcing fraud detection in a self-supervised manner. We deploy CMT to detect crowdsourcing frauds on an industry-size HTG of a representative MMMA WeChat and it significantly outperforms other methods. CMT also shows promising results for fraud detection on a large-scale public financial HTG, indicating that it can be applied in other graph anomaly detection tasks. We provide our implementation at https://github.com/KDEGroup/CMT. |
| title | Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2308.02793 |