# Label Information Enhanced Fraud Detection against Low Homophily in Graphs

arXiv: https://arxiv.org/abs/2302.10407 · HTML: https://mlsanity.com/paper/2302.10407/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [SIGN: Scalable Inception Graph Neural Networks](https://mlsanity.com/paper/2004.11198/) (2020)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Combining Label Propagation and Simple Models Out-performs Graph Neural Networks](https://mlsanity.com/paper/2010.13993/) (2020)
- [Graph Transformer for Graph-to-Sequence Learning](https://mlsanity.com/paper/1911.07470/) (2019)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [Spam Review Detection with Graph Convolutional Networks](https://mlsanity.com/paper/1908.10679/) (2019)
- [Bag of Tricks for Node Classification with Graph Neural Networks](https://mlsanity.com/paper/2103.13355/) (2021)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Robust Spammer Detection by Nash Reinforcement Learning](https://mlsanity.com/paper/2006.06069/) (2020)
- [Why Propagate Alone? Parallel Use of Labels and Features on Graphs](https://mlsanity.com/paper/2110.07190/) (2021)

## Built on by (most cited first, 22 of 22)

- [Deep Graph Anomaly Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2409.09957/) (2024)
- [A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection](https://mlsanity.com/paper/2502.13308/) (2025)
- [Partitioning Message Passing for Graph Fraud Detection](https://mlsanity.com/paper/2412.00020/) (2024)
- [Generative Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2402.11887/) (2024)
- [Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum](https://mlsanity.com/paper/2312.06441/) (2023)
- [A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions](https://mlsanity.com/paper/2401.09769/) (2024)
- [Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection](https://mlsanity.com/paper/2507.11997/) (2025)
- [Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2407.17333/) (2024)
- [Addressing Noise and Stochasticity in Fraud Detection for Service Networks](https://mlsanity.com/paper/2505.00946/) (2025)
- [Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning](https://mlsanity.com/paper/2507.06469/) (2025)
- [RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection](https://mlsanity.com/paper/2402.17472/) (2024)
- [Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection](https://mlsanity.com/paper/2505.18934/) (2025)
- [Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection](https://mlsanity.com/paper/2509.20339/) (2025)
- [Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection](https://mlsanity.com/paper/2605.27470/) (2026)
- [Graph as a feature: improving node classification with non-neural graph-aware logistic regression](https://mlsanity.com/paper/2411.12330/) (2024)
- [Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors](https://mlsanity.com/paper/2412.18370/) (2024)
- [SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection](https://mlsanity.com/paper/2501.12430/) (2025)
- [Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum](https://mlsanity.com/paper/2508.14684/) (2025)
- [AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation](https://mlsanity.com/paper/2602.17071/) (2026)
- [Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks](https://mlsanity.com/paper/2605.13863/) (2026)
- [Behavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoning](https://mlsanity.com/paper/2609.34211/) (2026)
- [Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes](https://mlsanity.com/paper/2609.38424/) (2026)

## Abstract (arXiv)

Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low homophily setting. Besides, label utilization has been proved to be significant factor for node classification problem. But we find they are less effective in fraud detection tasks due to the low homophily in graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced TrAnsformer, to tackle the above challenges. Specifically, the group aggregation provides a portable method to cope with the low homophily issue. Such an aggregation explicitly integrates the label information to generate distinguishable neighborhood information. Along with group aggregation, an attempt towards end-to-end trainable group encoding is proposed which augments the original feature space with the class labels. Meanwhile, we devise two additional learnable encodings to recognize the structural and relational context. Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information. Experimental results clearly show that GAGA outperforms other competitive graph-based fraud detectors by up to 24.39% on two trending public datasets and a real-world industrial dataset from Anonymous. Even more, the group aggregation is demonstrated to outperform other label utilization methods (e.g., C&S, BoT/UniMP) in the low homophily setting.