https://github.com/iheartgraph/illuminati
공식 코드Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis
Papers with Code 아카이브
Fraud Detection
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Fraud Detection is a vital topic that applies to many industries including the financial sectors, banking, government agencies, insurance, and law enforcement, and more. Fraud endeavors have detected a radical rise in current years, creating this topic more critical than ever. Despite struggles on the part of the troubled organizations, hundreds of millions of dollars are wasted to fraud each year. Because nearly a few samples confirm fraud in a vast community, locating these can be complex. Data mining and statistics help to predict and immediately distinguish fraud and take immediate action to minimize costs.
Source: Applying support vector data description for fraud detection
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/intelligence-csd-auth-gr/LionLearn/tree/master/LionForests_Multi
공식 코드https://github.com/ireydiak/anomaly_detection_nrcan
공식 코드A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms
https://github.com/jakobraymaekers/woe2.0
공식 코드https://github.com/jimmyma99/teleantifraud
공식 코드TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection
https://github.com/jtittelfitz/fpr-estimation
공식 코드FPR Estimation for Fraud Detection in the Presence of Class-Conditional Label Noise
https://github.com/jweihe/ADA-GAD
공식 코드ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
https://github.com/k-s-b/gnn_wco
공식 코드https://github.com/kaize0409/GPN
공식 코드Graph Prototypical Networks for Few-shot Learning on Attributed Networks
https://github.com/karayanni/distributed-ks-test
공식 코드Distributed Monitoring for Data Distribution Shifts in Edge-ML Fraud Detection
https://github.com/kdegroup/cmt
공식 코드Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph
https://github.com/kristian10007/convgen
공식 코드https://github.com/leo02016/fairmvc
공식 코드https://github.com/mountstonne/dggan
공식 코드Synthetic Demographic Data Generation for Card Fraud Detection Using GANs
https://github.com/nechbamohammed/swiftlogisticreg
공식 코드High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison
https://github.com/orion-wyc/gaga
공식 코드Label Information Enhanced Fraud Detection against Low Homophily in Graphs
https://github.com/project-basileus/multitype-sequence-generation-by-tlstm-gan
공식 코드Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem
https://github.com/pv-lab/zombi
공식 코드https://github.com/qci-github/eqc-studies
공식 코드https://github.com/qiurunwen/categoryencodercomparison
공식 코드https://github.com/quicksign/docxpand
공식 코드DocXPand-25k: a large and diverse benchmark dataset for identity documents analysis
https://github.com/ronaldpereira/brasnam-experiments
공식 코드How effective are Graph Neural Networks in Fraud Detection for Network Data?
https://github.com/safe-graph/DGFraud
공식 코드Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
https://github.com/safe-graph/RioGNN
공식 코드Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
https://github.com/sakanaai/edinet-bench
공식 코드EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
https://github.com/sakanaai/edinet2dataset
공식 코드EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
https://github.com/samanthav24/dsc_fraud_detection
공식 코드Explainable Fraud Detection with Deep Symbolic Classification
https://github.com/sattarov/FinDiff
공식 코드FinDiff: Diffusion Models for Financial Tabular Data Generation
https://github.com/seondong/customs-declaration-datasets
공식 코드https://github.com/sjecmen/peer-review-collusion-detection
공식 코드On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
https://github.com/snap-stanford/bc-emb
공식 코드https://github.com/somepago/saint
공식 코드SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
https://github.com/spring-epfl/transferable-cat-robustness
공식 코드Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
https://github.com/springwiz11/fed-gnodeformer
공식 코드Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs
https://github.com/sunxkissed/sec-gfd
공식 코드Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
https://github.com/tdlhl/RAGFormer
공식 코드RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.