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}.

Related papers

• Graph Neural Networks at a Fraction [1.8175282137722093]
  This paper introduces Quaternion Message Passing Neural Networks (QMPNNs), a framework that leverages quaternion space to compute node representations. We present a novel perspective on Graph Lottery Tickets, redefining their applicability within the context of GNNs and QMPNNs.
  arXiv  Detail & Related papers  (2025-02-10T03:55:09Z)
• Graph Spring Neural ODEs for Link Sign Prediction [49.71046810937725]
  We propose a novel message-passing layer architecture called Graph Spring Network (GSN) modeled after spring forces. We show that our method achieves accuracy close to the state-of-the-art methods with node generation time speedup factors of up to 28,000 on large graphs.
  arXiv  Detail & Related papers  (2024-12-17T13:50:20Z)
• Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
  We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
  arXiv  Detail & Related papers  (2023-05-18T07:27:18Z)
• Training Graph Neural Networks on Growing Stochastic Graphs [114.75710379125412]
  Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. We propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon.
  arXiv  Detail & Related papers  (2022-10-27T16:00:45Z)
• Geodesic Graph Neural Network for Efficient Graph Representation Learning [34.047527874184134]
  We propose an efficient GNN framework called Geodesic GNN (GDGNN) It injects conditional relationships between nodes into the model without labeling. Conditioned on the geodesic representations, GDGNN is able to generate node, link, and graph representations that carry much richer structural information than plain GNNs.
  arXiv  Detail & Related papers  (2022-10-06T02:02:35Z)
• Hyperbolic Graph Neural Networks: A Review of Methods and Applications [55.5502008501764]
  Graph neural networks generalize conventional neural networks to graph-structured data. The performance of Euclidean models in graph-related learning is still bounded and limited by the representation ability of Euclidean geometry. Recently, hyperbolic space has gained increasing popularity in processing graph data with tree-like structure and power-law distribution.
  arXiv  Detail & Related papers  (2022-02-28T15:08:48Z)
• ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network [72.16255675586089]
  We propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T07:18:57Z)
• AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting [0.38073142980733]
  Graph Neural Networks (GNNs) focus on extracting intrinsic network features. We propose boosting-based meta learner for GNNs. AdaGNN performs exceptionally well for applications with rich and diverse node neighborhood information.
  arXiv  Detail & Related papers  (2021-08-14T03:07:26Z)
• Increase and Conquer: Training Graph Neural Networks on Growing Graphs [116.03137405192356]
  We consider the problem of learning a graphon neural network (WNN) by training GNNs on graphs sampled Bernoulli from the graphon. Inspired by these results, we propose an algorithm to learn GNNs on large-scale graphs that, starting from a moderate number of nodes, successively increases the size of the graph during training.
  arXiv  Detail & Related papers  (2021-06-07T15:05:59Z)
• Isometric Graph Neural Networks [5.306334746787569]
  We propose a technique to learn Isometric Graph Neural Networks (IGNN) IGNN requires changing the input representation space and loss function to enable any GNN algorithm to generate representations that reflect distances between nodes. We observe a consistent and substantial improvement as high as 400% in Kendall's Tau (KT)
  arXiv  Detail & Related papers  (2020-06-16T22:51:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.