72 citations · 140 across the 13 of their papers we have counts for
14 papers
SymCERE: Symmetric Contrastive Learning for Robust Review-Enhanced Recommendation
Toyotaro Suzumura, Hisashi Ikari, Hiroki Kanezashi +2
Modern recommendation systems fuse user behavior graphs and review texts but often encounter a "Fusion Gap" caused by False Negatives, Popularity Bias, and Signal Ambiguity. We pro…
GEFM: Graph-Enhanced EEG Foundation Model
Limin Wang, Toyotaro Suzumura, Hiroki Kanezashi
Electroencephalography (EEG) signals provide critical insights for applications in disease diagnosis and healthcare. However, the scarcity of labeled EEG data poses a significant c…
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning
Toyotaro Suzumura, Hiroki Kanezashi, Shotaro Akahori
In diagnosing neurological disorders from electroencephalography (EEG) data, foundation models such as Transformers have been employed to capture temporal dynamics. Additionally, G…
Multimodal Point-of-Interest Recommendation
Yuta Kanzawa, Toyotaro Suzumura, Hiroki Kanezashi +2
Large Language Models are applied to recommendation tasks such as items to buy and news articles to read. Point of Interest is quite a new area to sequential recommendation based o…
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
LLM-jp, :, Akiko Aizawa +80
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Xiaohang Xu, Toyotaro Suzumura, Jiawei Yong +5
Next Point-of-Interest (POI) recommendation plays a crucial role in urban mobility applications. Recently, POI recommendation models based on Graph Neural Networks (GNN) have been…
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks
Hiroki Kanezashi, Toyotaro Suzumura, Xin Liu +1
While transactions with cryptocurrencies such as Ethereum are becoming more prevalent, fraud and other criminal transactions are not uncommon. Graph analysis algorithms and machine…
How Expressive are Transformers in Spectral Domain for Graphs?
Anson Bastos, Abhishek Nadgeri, Kuldeep Singh +3
The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning. To understand this inadequacy, t…
Global Data Science Project for COVID-19
Toyotaro Suzumura, Dario Garcia-Gasulla, Sergio Alvarez Napagao +14
This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both dire…
The Impact of COVID-19 on Flight Networks
Toyotaro Suzumura, Hiroki Kanezashi, Mishal Dholakia +5
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections. Such restrictions have a direct effect on the…
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen +6
Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neu…
Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
Hiroki Kanezashi, Toyotaro Suzumura, Dario Garcia-Gasulla +2
Graph pattern matching algorithms to handle million-scale dynamic graphs are widely used in many applications such as social network analytics and suspicious transaction detections…
Scalable Graph Learning for Anti-Money Laundering: A First Look
Mark Weber, Jie Chen, Toyotaro Suzumura +6
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" i…
System G Distributed Graph Database
Gabriel Tanase, Toyotaro Suzumura, Jinho Lee +5
Motivated by the need to extract knowledge and value from interconnected data, graph analytics on big data is a very active area of research in both industry and academia. To suppo…