72 citations · 140 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…