hkanezashi

AMLSim

The AMLSim project is intended to provide a multi-agent based simulator that generates a series of banking transaction data together with a set of known money laundering patterns - mainly for the purpose of testing your machine learning models and graph algorithms. We welcome you to enhance this effort since data set is critical to advance our detection capabilities of money laundering activities .

deep-learning-from-scratch

『ゼロから作る Deep Learning』のリポジトリ（Neural Network モデルの拡張、アプリケーションの追加も）

EvolveGCN

Code for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

GVTest

Graph Visualization Test

IGPM-PEM

Scalable and Approximate Pattern Matching for Billion-Scale Property Graphs

Inductive-representation-learning-on-temporal-graphs

MGL

ogb

Benchmark datasets, data loaders, and evaluators for graph machine learning

PaySim

Financial Simulator of Mobile Money Service

SGC

official implementation for the paper "Simplifying Graph Convolutional Networks"

hkanezashi/PaySim

Financial Simulator of Mobile Money Service

hkanezashi/AMLSim

The AMLSim project is intended to provide a multi-agent based simulator that generates a series of banking transaction data together with a set of known money laundering patterns - mainly for the purpose of testing your machine learning models and graph algorithms. We welcome you to enhance this effort since data set is critical to advance our detection capabilities of money laundering activities .

hkanezashi/deep-learning-from-scratch

『ゼロから作る Deep Learning』のリポジトリ（Neural Network モデルの拡張、アプリケーションの追加も）

hkanezashi/EvolveGCN

Code for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

hkanezashi/GVTest

Graph Visualization Test

hkanezashi/IGPM-PEM

Scalable and Approximate Pattern Matching for Billion-Scale Property Graphs

hkanezashi/Inductive-representation-learning-on-temporal-graphs

hkanezashi/MGL

hkanezashi/ogb

Benchmark datasets, data loaders, and evaluators for graph machine learning

hkanezashi/SGC

official implementation for the paper "Simplifying Graph Convolutional Networks"