Search Results for author: Yingtong Dou

Found 12 papers, 10 papers with code

Deep Fraud Detection on Non-attributed Graph

no code implementations4 Oct 2021 Chen Wang, Yingtong Dou, Min Chen, Jia Chen, Zhiwei Liu, Philip S. Yu

The successes of most previous methods heavily rely on rich node features and high-fidelity labels.

Contrastive Learning Fraud Detection

User Preference-aware Fake News Detection

1 code implementation25 Apr 2021 Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun

The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.

Fact Checking Fake News Detection +2

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

1 code implementation16 Apr 2021 Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, Philip S. Yu

To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes.

Fraud Detection Node Classification +1

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

1 code implementation16 Apr 2021 JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

Node Classification Node Clustering +1

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

2 code implementations21 Jan 2021 Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, JianXin Li, Philip S. Yu

The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.

Event Detection Feature Engineering +3

Robust Spammer Detection by Nash Reinforcement Learning

1 code implementation10 Jun 2020 Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie

We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal.

Fraud Detection reinforcement-learning

Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

1 code implementation1 May 2020 Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng

In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes.

Fraud Detection

Uncovering Download Fraud Activities in Mobile App Markets

no code implementations5 Jul 2019 Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu

To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.

Adversarial Attack and Defense on Graph Data: A Survey

1 code implementation26 Dec 2018 Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, Bo Li

Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph.

Adversarial Attack Image Classification +1

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