mlsanityData to 7 Oct 2026

Spam Review Detection with Graph Convolutional Networks

arXiv 1908.10679 ↗First posted 22 Aug 2019cs.IR cs.LG cs.NIcited by 57

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Abstract (arXiv)

Customers make a lot of reviews on online shopping websites every day, e.g., Amazon and Taobao. Reviews affect the buying decisions of customers, meanwhile, attract lots of spammers aiming at misleading buyers. Xianyu, the largest second-hand goods app in China, suffering from spam reviews. The anti-spam system of Xianyu faces two major challenges: scalability of the data and adversarial actions taken by spammers. In this paper, we present our technical solutions to address these challenges. We propose a large-scale anti-spam method based on graph convolutional networks (GCN) for detecting spam advertisements at Xianyu, named GCN-based Anti-Spam (GAS) model. In this model, a heterogeneous graph and a homogeneous graph are integrated to capture the local context and global context of a comment. Offline experiments show that the proposed method is superior to our baseline model in which the information of reviews, features of users and items being reviewed are utilized. Furthermore, we deploy our system to process million-scale data daily at Xianyu. The online performance also demonstrates the effectiveness of the proposed method.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2019.

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Citing papers per year: 2021: 12; 2022: 16; 2023: 14; 2024: 6; 2025: 5; 2026: 4.

57 papers.

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  1. 2021A Review on Graph Neural Network Methods in Financial Applicationscited by 77
  2. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  3. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  4. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  5. 2021Accelerating Large Scale Real-Time GNN Inference using Channel Pruningcited by 41
  6. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  7. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  8. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  9. 2023Graph Condensation for Inductive Node Representation Learningcited by 31
  10. 2021Graph Contrastive Learning for Anomaly Detectioncited by 22
  11. 2023Label Information Enhanced Fraud Detection against Low Homophily in Graphscited by 22
  12. 2023Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detectioncited by 19
38 more
  1. 2021A Biased Graph Neural Network Sampler with Near-Optimal Regretcited by 17
  2. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  3. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  4. 2022Deep Graph Learning for Anomalous Citation Detectioncited by 14
  5. 2024UniGAD: Unifying Multi-level Graph Anomaly Detectioncited by 13
  6. 2022Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGAcited by 12
  7. 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protectioncited by 12
  8. 2022The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detectioncited by 11
  9. 2021Network In Graph Neural Networkcited by 8
  10. 2023Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagationcited by 8
  11. 2022Efficient Graph Neural Network Inference at Large Scalecited by 7
  12. 2025DiffGraph: Heterogeneous Graph Diffusion Modelcited by 6
  13. 2022Graph Neural Networks: a bibliometrics overviewcited by 5
  14. 2022Improving Fraud Detection via Hierarchical Attention-based Graph Neural Networkcited by 5
  15. 2022Modelling graph dynamics in fraud detection with "Attention"cited by 5
  16. 2022Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Servicescited by 5
  17. 2023GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detectioncited by 5
  18. 2023Graph Mining for Cybersecurity: A Surveycited by 5
  19. 2023Investigating the Interplay between Features and Structures in Graph Learningcited by 5
  20. 2023Robust Graph Clustering via Meta Weighting for Noisy Graphscited by 5
  21. 2024Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attackscited by 5
  22. 2021Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representationcited by 4
  23. 2021HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Featurescited by 4
  24. 2021Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networkscited by 4
  25. 2024KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Datacited by 4
  26. 2021Deep Fraud Detection on Non-attributed Graphcited by 3
  27. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  28. 2022ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Casescited by 2
  29. 2026Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detectioncited by 2
  30. 2022Learning Personalized Representations using Graph Convolutional Networkcited by 1
  31. 2022PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processescited by 1
  32. 2023Railway Network Delay Evolution: A Heterogeneous Graph Neural Network Approachcited by 1
  33. 2023AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUscited by 1
  34. 2023STRIDE: Structure and Embedding Distillation with Attention for Graph Neural Networkscited by 1
  35. 2023Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Modelscited by 1
  36. 2024TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scalecited by 1
  37. 2022Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detectioncited by 0
  38. 2022Signed Latent Factors for Spamming Activity Detectioncited by 0

50 most cited of 57 All 57 (JSON)