Search Results for author: Hiroki Kanezashi

Found 8 papers, 4 papers with code

Ethereum Fraud Detection with Heterogeneous Graph Neural Networks

no code implementations23 Mar 2022 Hiroki Kanezashi, Toyotaro Suzumura, Xin Liu, Takahiro Hirofuchi

Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges.

Fraud Detection

How Expressive are Transformers in Spectral Domain for Graphs?

1 code implementation23 Jan 2022 Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Hiroki Kanezashi, Toyotaro Suzumura, Isaiah Onando Mulang'

We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.

Graph Representation Learning

The Impact of COVID-19 on Flight Networks

no code implementations4 Jun 2020 Toyotaro Suzumura, Hiroki Kanezashi, Mishal Dholakia, Euma Ishii, Sergio Alvarez Napagao, Raquel Pérez-Arnal, Dario Garcia-Gasulla, Toshiaki Murofushi

As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections.

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

8 code implementations26 Feb 2019 Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, Charles E. Leiserson

Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.

Dynamic Link Prediction Edge Classification +3

Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs

1 code implementation21 Dec 2018 Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla, Min-hwan Oh, Satoshi Matsuoka

We propose an incremental graph pattern matching algorithm to deal with time-evolving graph data and also propose an adaptive optimization system based on reinforcement learning to recompute vertices in the incremental process more efficiently.


Scalable Graph Learning for Anti-Money Laundering: A First Look

2 code implementations30 Nov 2018 Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl

Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.

Graph Learning

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