Quaternion Graph Neural Networks
- URL: http://arxiv.org/abs/2008.05089v6
- Date: Thu, 7 Oct 2021 02:35:09 GMT
- Title: Quaternion Graph Neural Networks
- Authors: Dai Quoc Nguyen and Tu Dinh Nguyen and Dinh Phung
- Abstract summary: We propose Quaternion Graph Neural Networks (QGNN) to learn graph representations within the Quaternion space.
Our QGNN obtains state-of-the-art results on a range of benchmark datasets for graph classification and node classification.
- Score: 17.10479440152652
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, graph neural networks (GNNs) have become an important and active
research direction in deep learning. It is worth noting that most of the
existing GNN-based methods learn graph representations within the Euclidean
vector space. Beyond the Euclidean space, learning representation and
embeddings in hyper-complex space have also shown to be a promising and
effective approach. To this end, we propose Quaternion Graph Neural Networks
(QGNN) to learn graph representations within the Quaternion space. As
demonstrated, the Quaternion space, a hyper-complex vector space, provides
highly meaningful computations and analogical calculus through Hamilton product
compared to the Euclidean and complex vector spaces. Our QGNN obtains
state-of-the-art results on a range of benchmark datasets for graph
classification and node classification. Besides, regarding knowledge graphs,
our QGNN-based embedding model achieves state-of-the-art results on three new
and challenging benchmark datasets for knowledge graph completion. Our code is
available at: \url{https://github.com/daiquocnguyen/QGNN}.
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