600 citations · 1.6k across the 17 of their papers we have counts for
17 papers
Personalized Layer Selection for Graph Neural Networks
Kartik Sharma, Vineeth Rakesh, Yingtong Dou +2
Graph Neural Networks (GNNs) combine node attributes over a fixed granularity of the local graph structure around a node to predict its label. However, different nodes may relate t…
Attacking Fake News Detectors via Manipulating News Social Engagement
Haoran Wang, Yingtong Dou, Canyu Chen +3
Social media is one of the main sources for news consumption, especially among the younger generation. With the increasing popularity of news consumption on various social media pl…
Automating DBSCAN via Deep Reinforcement Learning
Ruitong Zhang, Hao Peng, Yingtong Dou +4
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality. However, due to its high sensitivity parameters, the accuracy of the clu…
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao +12
Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years…
PyGOD: A Python Library for Graph Outlier Detection
Kay Liu, Yingtong Dou, Xueying Ding +5
PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based meth…
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…
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…
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…
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…
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…
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…
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
Yuwei Cao, Hao Peng, Jia Wu +3
Social events provide valuable insights into group social behaviors and public concerns and therefore have many applications in fields such as product recommendation and crisis man…
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Yingtong Dou, Zhiwei Liu, Li Sun +3
Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood informati…
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
Zhiwei Liu, Yingtong Dou, Philip S. Yu +2
The graph-based model can help to detect suspicious fraud online. Owing to the development of Graph Neural Networks~(GNNs), prior research work has proposed many GNN-based fraud de…
Robust Spammer Detection by Nash Reinforcement Learning
Yingtong Dou, Guixiang Ma, Philip S. Yu +1
Online reviews provide product evaluations for customers to make decisions. Unfortunately, the evaluations can be manipulated using fake reviews ("spams") by professional spammers,…
Uncovering Download Fraud Activities in Mobile App Markets
Yingtong Dou, Weijian Li, Zhirong Liu +3
Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads ca…
Adversarial Attack and Defense on Graph Data: A Survey
Lichao Sun, Yingtong Dou, Carl Yang +5
Deep neural networks (DNNs) have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However,…