heterogeneous graph

DataFunTalk

May 27, 2023 · Artificial Intelligence

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

The article presents a comprehensive overview of how graph algorithms are applied in Alibaba's e‑commerce risk control system, detailing six sections that include risk scenario introductions, interaction and product content risk methods, dynamic heterogeneous graph practices, a large‑scale competition, and future research directions.

Dynamic GraphGraph Neural Networkse-commerce risk

0 likes · 18 min read

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

Ctrip Technology

May 25, 2023 · Artificial Intelligence

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

This article presents a graph‑neural‑network driven, unsupervised approach that builds heterogeneous user‑feature graphs, learns node weights, constructs user‑user similarity graphs, and applies threshold‑based clustering to identify abnormal registration clusters for fraud detection in Ctrip's business travel platform.

Anomaly Detectionfraud detectiongraph neural network

0 likes · 12 min read

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

DataFunTalk

Sep 10, 2022 · Artificial Intelligence

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

This article reviews how graph neural networks are applied across the three stages of recommendation systems—recall, ranking, and re‑ranking—detailing novel models such as NIA‑GCN, GraphSAIL, and DGENN, their experimental improvements, and future research directions.

GNN recallGraph Neural NetworksRanking

0 likes · 17 min read

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

DataFunSummit

Jan 26, 2022 · Artificial Intelligence

Applying Graph Neural Networks for Early Fraud Warning and Malicious URL Detection

This article explains how Tencent's security data lab uses graph neural networks to build heterogeneous temporal graphs for early warning of water‑room fraud cards and to detect malicious URLs, detailing the data modeling, graph construction, attention‑based aggregation, model training, and evaluation results.

AI securityGraph Neural NetworksMachine Learning

0 likes · 8 min read

Applying Graph Neural Networks for Early Fraud Warning and Malicious URL Detection

ByteDance Terminal Technology

Jan 11, 2022 · Information Security

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Work

This article presents a comprehensive study on detecting malicious webpages using heterogeneous graph structures and Graph Convolutional Networks, detailing background challenges, technical approaches, model iterations, optimization techniques for large‑scale deployment, experimental results, and directions for future research.

DetectionGCNGraph Neural Networks

0 likes · 9 min read

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Work

ByteDance Terminal Technology

Jan 7, 2022 · Information Security

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions

This article presents a comprehensive study on detecting malicious webpages by constructing heterogeneous graphs from URL redirection and textual features, applying Graph Convolutional Networks and Cluster‑Text‑GCN models, detailing optimization techniques for large‑scale deployment, and outlining future research directions.

GCNGraph Neural NetworksText Classification

0 likes · 11 min read

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions

DataFunSummit

Sep 6, 2021 · Artificial Intelligence

Graph Neural Network‑Based Payment Fraud Detection at eBay

This article explains how eBay uses graph neural networks and a heterogeneous‑graph fraud detection framework (xFraud) to improve payment risk assessment, overcome the limitations of traditional machine‑learning models, and effectively identify both individual and organized fraud in a large‑scale e‑commerce environment.

Dynamic GraphGraph Neural NetworksMachine Learning

0 likes · 15 min read

Graph Neural Network‑Based Payment Fraud Detection at eBay

DataFunTalk

Apr 16, 2021 · Artificial Intelligence

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

This article presents a comprehensive overview of Alibaba's 1688 live‑streaming recommendation system, detailing core challenges such as heterogeneous behavior modeling, multi‑objective optimization, and bias mitigation, and describing four successive model iterations—from feature‑engineered GBDT to attention‑based heterogeneous networks and transformer architectures—along with experimental results and practical insights.

Bias MitigationLive StreamingRecommendation systems

0 likes · 22 min read

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

iQIYI Technical Product Team

Oct 30, 2020 · Artificial Intelligence

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

HMGNN introduces hyper-nodes to connect many small heterogeneous mini-graphs, uses attention-weighted heterogeneous convolution and residual feature transmission, achieving superior fraud invitation detection on iQIYI and Cora datasets compared to traditional GNNs and other models.

Hypergraphaifraud detection

0 likes · 10 min read

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

AntTech

Nov 1, 2018 · Artificial Intelligence

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

This article introduces the GEM method, the first heterogeneous graph neural network designed for malicious account detection, explains the nature and characteristics of malicious accounts, describes why graph neural networks are effective, and presents experimental results from the authors' CIKM 2018 study.

AI securityCIKM 2018Graph Neural Networks

0 likes · 8 min read

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper