# Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection

arXiv: https://arxiv.org/abs/2303.17486 · HTML: https://mlsanity.com/paper/2303.17486/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 74 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [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)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://mlsanity.com/paper/1802.00910/) (2018)
- [GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction](https://mlsanity.com/paper/2106.11133/) (2021)
- [Imbalanced Graph Classification via Graph-of-Graph Neural Networks](https://mlsanity.com/paper/2112.00238/) (2021)
- [Network Embedding with Completely-imbalanced Labels](https://mlsanity.com/paper/2007.03545/) (2020)
- [Graph Neural Network with Curriculum Learning for Imbalanced Node Classification](https://mlsanity.com/paper/2202.02529/) (2022)
- [Deep Iterative and Adaptive Learning for Graph Neural Networks](https://mlsanity.com/paper/1912.07832/) (2019)
- [Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification](https://mlsanity.com/paper/2110.12035/) (2021)
- [Neural Similarity Learning](https://mlsanity.com/paper/1910.13003/) (2019)

## Built on by (most cited first, 12 of 12)

- [Graph Neural Networks for Financial Fraud Detection: A Review](https://mlsanity.com/paper/2411.05815/) (2024)
- [GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection](https://mlsanity.com/paper/2303.17334/) (2023)
- [Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2305.02901/) (2023)
- [CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection](https://mlsanity.com/paper/2605.20032/) (2026)
- [Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classification](https://mlsanity.com/paper/2412.00238/) (2024)
- [Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning](https://mlsanity.com/paper/2507.09132/) (2025)
- [SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks](https://mlsanity.com/paper/2408.16537/) (2024)
- [Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin](https://mlsanity.com/paper/2502.11505/) (2025)
- [Corporate Fraud Detection in Rich-yet-Noisy Financial Graph](https://mlsanity.com/paper/2502.19305/) (2025)
- [Supervised Graph Contrastive Learning for Gene Regulatory Networks](https://mlsanity.com/paper/2505.17786/) (2025)
- [SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts](https://mlsanity.com/paper/2509.05681/) (2025)
- [Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks](https://mlsanity.com/paper/2605.13863/) (2026)

## Abstract (arXiv)

With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail record (CDR) data, which portrays the social behavior of users in mobile networks, has been widely utilized. But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work. In this paper, we are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural networks. We conduct extensive experiments on two open-source realworld mobile network fraud datasets. The results show that CSGNN can effectively solve the graph imbalance problem and then achieve better detection performance than the state-of-the-art algorithms. We believe that our research can be applied to solve the graph imbalance problems in other fields. The CSGNN code and datasets are publicly available at https://github.com/xxhu94/CSGNN.