AAAI 2026technical0 citations
Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs
Jeongwhan Choi, Seungjun Park, Sumin Park, Sung-Bae Cho, Noseong Park
Abstract
Graph Neural Networks (GNNs) have emerged as powerful tools for learning on graph-structured data, but often struggle to balance local and global information. While graph Transformers aim to address this by enabling long-range interactions, they often overlook the inherent locality and efficiency of Message Passing Neural Networks (MPNNs). We propose a new concept called
BibTeX
@inproceedings{aaai2026_aregraphtransfor,
title = {Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs},
author = {Jeongwhan Choi and Seungjun Park and Sumin Park and Sung-Bae Cho and Noseong Park},
booktitle = {AAAI 2026},
year = {2026}
}