Toyotaro Suzumura

research

∙ 01/30/2023

Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?

In this paper we present a novel method, Knowledge Persistence (𝒦𝒫), for...

Anson Bastos, et al. ∙

research

∙ 12/12/2022

MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling

Spatio-temporal modeling as a canonical task of multivariate time series...

Renhe Jiang, et al. ∙

research

∙ 11/27/2022

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

Traffic forecasting as a canonical task of multivariate time series fore...

Renhe Jiang, et al. ∙

research

∙ 11/22/2022

Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions

Learning on evolving(dynamic) graphs has caught the attention of researc...

Anson Bastos, et al. ∙

research

∙ 05/23/2022

KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation

Leveraging graphs on recommender systems has gained popularity with the ...

Daisuke Kikuta, et al. ∙

research

∙ 03/27/2022

mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

The growing amount of data and advances in data science have created a n...

Toyotaro Suzumura, et al. ∙

research

∙ 03/23/2022

Ethereum Fraud Detection with Heterogeneous Graph Neural Networks

While transactions with cryptocurrencies such as Ethereum are becoming m...

Hiroki Kanezashi, et al. ∙

research

∙ 01/23/2022

Investigating Expressiveness of Transformer in Spectral Domain for Graphs

Transformers have been proven to be inadequate for graph representation ...

Anson Bastos, et al. ∙

research

∙ 09/16/2021

Efficient Scaling of Dynamic Graph Neural Networks

We present distributed algorithms for training dynamic Graph Neural Netw...

Venkatesan T. Chakaravarthy, et al. ∙

research

∙ 05/21/2021

Finding All Bounded-Length Simple Cycles in a Directed Graph

A new efficient algorithm is presented for finding all simple cycles tha...

Anshul Gupta, et al. ∙

research

∙ 03/26/2021

Heterogeneous Graph Neural Networks for Multi-label Text Classification

Multi-label text classification (MLTC) is an attractive and challenging ...

Irene Li, et al. ∙

research

∙ 01/18/2021

Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling

The dynamic scaling of distributed computations plays an important role ...

Masatoshi Hanai, et al. ∙

research

∙ 06/04/2020

The Impact of COVID-19 on Flight Networks

As COVID-19 transmissions spread worldwide, governments have announced a...

Toyotaro Suzumura, et al. ∙

research

∙ 05/26/2020

Benchmarking Graph Data Management and Processing Systems: A Survey

The development of scalable, representative, and widely adopted benchmar...

Miyuru Dayarathna, et al. ∙

research

∙ 12/04/2019

Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincaré Embeddings

In the recent years money laundering schemes have grown in complexity an...

Lucia Larise Stavarache, et al. ∙

research

∙ 09/24/2019

Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis

Recently, there has been a surge of interest in the use of machine learn...

Daiki Matsunaga, et al. ∙

research

∙ 09/19/2019

Towards Federated Graph Learning for Collaborative Financial Crimes Detection

Financial crime is a large and growing problem, in some way touching alm...

Toyotaro Suzumura, et al. ∙

research

∙ 08/16/2019

Distributed Edge Partitioning for Trillion-edge Graphs

We propose Distributed Neighbor Expansion (Distributed NE), a parallel a...

Masatoshi Hanai, et al. ∙

research

∙ 02/26/2019

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph representation learning resurges as a trending research subject ow...

Aldo Pareja, et al. ∙

research

∙ 12/21/2018

Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs

Graph pattern matching algorithms to handle million-scale dynamic graphs...

Hiroki Kanezashi, et al. ∙

research

∙ 11/30/2018

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

Organized crime inflicts human suffering on a genocidal scale: the Mexic...

Mark Weber, et al. ∙

research

∙ 08/19/2018

An incremental local-first community detection method for dynamic graphs

Community detections for large-scale real world networks have been more ...

Hiroki Kanezashi, et al. ∙

research

∙ 04/17/2018

Scalable attribute-aware network embedding with locality

Adding attributes for nodes to network embedding helps to improve the ab...

Weiyi Liu, et al. ∙

research

∙ 04/17/2018

Scalable attribute-aware network embedding with localily

Adding attributes for nodes to network embedding helps to improve the ab...

Weiyi Liu, et al. ∙

research

∙ 02/08/2018

System G Distributed Graph Database

Motivated by the need to extract knowledge and value from interconnected...

Gabriel Tanase, et al. ∙

research

∙ 07/24/2017

Full-Network Embedding in a Multimodal Embedding Pipeline

The current state-of-the-art for image annotation and image retrieval ta...

Armand Vilalta, et al. ∙

research

∙ 07/24/2017

Building Graph Representations of Deep Vector Embeddings

Patterns stored within pre-trained deep neural networks compose large an...

Dario Garcia-Gasulla, et al. ∙

research

∙ 07/19/2017

Can GAN Learn Topological Features of a Graph?

This paper is first-line research expanding GANs into graph topology ana...

Weiyi Liu, et al. ∙

research

∙ 05/22/2017

An Out-of-the-box Full-network Embedding for Convolutional Neural Networks

Transfer learning for feature extraction can be used to exploit deep rep...

Dario Garcia-Gasulla, et al. ∙

research

∙ 04/22/2017

Medical Text Classification using Convolutional Neural Networks

We present an approach to automatically classify clinical text at a sent...

Mark Hughes, et al. ∙

research

∙ 03/03/2017

On the Behavior of Convolutional Nets for Feature Extraction

Deep neural networks are representation learning techniques. During trai...

Dario Garcia-Gasulla, et al. ∙

research

∙ 05/18/2015

Scalable Parallel Numerical Constraint Solver Using Global Load Balancing

We present a scalable parallel solver for numerical constraint satisfact...

Daisuke Ishii, et al. ∙

research

∙ 11/06/2014

Scalable Parallel Numerical CSP Solver

We present a parallel solver for numerical constraint satisfaction probl...

Daisuke Ishii, et al. ∙

Toyotaro Suzumura

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