activity
20192021
most citedEnhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

600 citations · 916 across the 7 of their papers we have counts for

collaborators

11 papers

cs.CL20211 cited

Cross-lingual COVID-19 Fake News Detection

Jiangshu Du, Yingtong Dou, Congying Xia +3

The COVID-19 pandemic poses a great threat to global public health. Meanwhile, there is massive misinformation associated with the pandemic which advocates unfounded or unscientifi…

cs.LG2021

Deep Fraud Detection on Non-attributed Graph

Chen Wang, Yingtong Dou, Min Chen +3

Fraud detection problems are usually formulated as a machine learning problem on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance on fraud detection. Th…

cs.SI2021

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

Liangwei Yang, Zhiwei Liu, Yingtong Dou +2

Social recommendation aims to fuse social links with user-item interactions to alleviate the cold-start problem for rating prediction. Recent developments of Graph Neural Networks…

cs.SI20212 cited

User Preference-aware Fake News Detection

Yingtong Dou, Kai Shu, Congying Xia +2

Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing…

cs.LG202116 cited

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

Jianxin Li, Hao Peng, Yuwei Cao +4

Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis ta…

cs.LG2021

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

Hao Peng, Ruitong Zhang, Yingtong Dou +3

Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data. While promising, most existing GNNs oversimplified the complexi…