EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks

• URL: http://arxiv.org/abs/2505.05650v1
• Date: Thu, 08 May 2025 21:11:05 GMT
• Title: EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks
• Authors: Tien Dang, Truong-Son Hy,
• Abstract summary: We introduce EquiHGNN, a framework that integrates symmetry-aware representations to improve molecular modeling.<n>Our approach preserves geometric and topological properties, leading to more robust and physically meaningful representations.<n> Experiments on both small and large molecules show that high-order interactions offer limited benefits for small molecules but consistently outperform 2D graphs on larger ones.
• Score: 1.7034813545878589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular interactions often involve high-order relationships that cannot be fully captured by traditional graph-based models limited to pairwise connections. Hypergraphs naturally extend graphs by enabling multi-way interactions, making them well-suited for modeling complex molecular systems. In this work, we introduce EquiHGNN, an Equivariant HyperGraph Neural Network framework that integrates symmetry-aware representations to improve molecular modeling. By enforcing the equivariance under relevant transformation groups, our approach preserves geometric and topological properties, leading to more robust and physically meaningful representations. We examine a range of equivariant architectures and demonstrate that integrating symmetry constraints leads to notable performance gains on large-scale molecular datasets. Experiments on both small and large molecules show that high-order interactions offer limited benefits for small molecules but consistently outperform 2D graphs on larger ones. Adding geometric features to these high-order structures further improves the performance, emphasizing the value of spatial information in molecular learning. Our source code is available at https://github.com/HySonLab/EquiHGNN/

Related papers

• Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs [66.98487644676906]
  We introduce Neural P$3$M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities. It exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures.
  arXiv  Detail & Related papers  (2024-09-26T08:16:59Z)
• SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning [27.713870291922333]
  We develop an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. SE3Set has shown performance on par with state-of-the-art (SOTA) models for small molecule datasets. It excels on the MD22 dataset, achieving a notable improvement of approximately 20% in accuracy across all molecules.
  arXiv  Detail & Related papers  (2024-05-26T10:43:16Z)
• Hyperbolic Graph Diffusion Model [24.049660417511074]
  We propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM) HGDM consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. Experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a $48%$ improvement in the quality of graph generation with highly hierarchical structures.
  arXiv  Detail & Related papers  (2023-06-13T08:22:18Z)
• Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation [32.66694406638287]
  We propose a new joint 2D and 3D diffusion model (JODO) that generates molecules with atom types, formal charges, bond information, and 3D coordinates. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties.
  arXiv  Detail & Related papers  (2023-05-21T04:49:53Z)
• Simple and Efficient Heterogeneous Graph Neural Network [55.56564522532328]
  Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN)
  arXiv  Detail & Related papers  (2022-07-06T10:01:46Z)
• Dist2Cycle: A Simplicial Neural Network for Homology Localization [66.15805004725809]
  Simplicial complexes can be viewed as high dimensional generalizations of graphs that explicitly encode multi-way ordered relations. We propose a graph convolutional model for learning functions parametrized by the $k$-homological features of simplicial complexes.
  arXiv  Detail & Related papers  (2021-10-28T14:59:41Z)
• Molecular Graph Generation via Geometric Scattering [7.796917261490019]
  Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. We propose a representation-first approach to molecular graph generation. We show that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis.
  arXiv  Detail & Related papers  (2021-10-12T18:00:23Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)
• Parameterized Hypercomplex Graph Neural Networks for Graph Classification [1.1852406625172216]
  We develop graph neural networks that leverage the properties of hypercomplex feature transformation. In particular, in our proposed class of models, the multiplication rule specifying the algebra itself is inferred from the data during training. We test our proposed hypercomplex GNN on several open graph benchmark datasets and show that our models reach state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-30T18:01:06Z)
• E(n) Equivariant Graph Neural Networks [86.75170631724548]
  This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs) In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance.
  arXiv  Detail & Related papers  (2021-02-19T10:25:33Z)
• Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures [20.276492931562036]
  A growing number of Graph Neural Networks (GNNs) have been proposed to address this challenge. In this work, we aim to design a GNN which is both powerful and efficient for molecule structures. We build Multiplex Molecular Graph Neural Network (MXMNet)
  arXiv  Detail & Related papers  (2020-11-15T05:55:15Z)
• Multi-View Graph Neural Networks for Molecular Property Prediction [67.54644592806876]
  We present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture. In MV-GNN, we introduce a shared self-attentive readout component and disagreement loss to stabilize the training process. We further boost the expressive power of MV-GNN by proposing a cross-dependent message passing scheme.
  arXiv  Detail & Related papers  (2020-05-17T04:46:07Z)

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.