dynamic graphs | BestHub

JD Tech Talk

Dec 3, 2020 · Artificial Intelligence

Graph Algorithms and Graph Neural Networks for Fraud Detection

The article explains how building account relationship graphs and applying both traditional graph algorithms and modern graph neural networks—through community detection, anomaly detection, semi‑supervised and unsupervised learning, and dynamic graph techniques—can effectively identify and dismantle fraud groups in online services.

AIGraph Neural NetworksSemi-supervised Learning

0 likes · 11 min read

Graph Algorithms and Graph Neural Networks for Fraud Detection

DataFunTalk

Oct 17, 2020 · Artificial Intelligence

DyHAN: Dynamic Heterogeneous Graph Embedding with Hierarchical Attention

This article introduces DyHAN, a dynamic heterogeneous graph embedding method that employs hierarchical attention across node, edge, and temporal dimensions to capture evolving user-item interactions, demonstrates superior performance over static and existing dynamic baselines, and reports significant online improvements in Alibaba’s recommendation system.

Alibabaattention mechanismdynamic graphs

0 likes · 9 min read

DyHAN: Dynamic Heterogeneous Graph Embedding with Hierarchical Attention

AntTech

Dec 20, 2017 · Artificial Intelligence

Network Embedding Overview and Recent Research Directions from CIKM 2017

An overview of network embedding presented at CIKM 2017, covering its definition, loss functions, algorithm categories such as spectral methods, random walks, deep learning models, emerging research topics like dynamic and attributed embeddings, and various application scenarios illustrated with numerous academic papers.

CIKM2017Deep Learningattribute integration

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Network Embedding Overview and Recent Research Directions from CIKM 2017