Permutation Equivariant Generative Adversarial Networks for Graphs

• URL: http://arxiv.org/abs/2112.03621v1
• Date: Tue, 7 Dec 2021 10:37:49 GMT
• Title: Permutation Equivariant Generative Adversarial Networks for Graphs
• Authors: Yoann Boget, Magda Gregorova, Alexandros Kalousis
• Abstract summary: We propose 3G-GAN, a 3-stages model relying on GANs and equivariant functions. We present some encouraging exploratory experiments and discuss the issues still to be addressed.
• Score: 72.20409648915398
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: One of the most discussed issues in graph generative modeling is the ordering of the representation. One solution consists of using equivariant generative functions, which ensure the ordering invariance. After having discussed some properties of such functions, we propose 3G-GAN, a 3-stages model relying on GANs and equivariant functions. The model is still under development. However, we present some encouraging exploratory experiments and discuss the issues still to be addressed.

Related papers

• Discrete Graph Auto-Encoder [52.50288418639075]
  We introduce a new framework named Discrete Graph Auto-Encoder (DGAE) We first use a permutation-equivariant auto-encoder to convert graphs into sets of discrete latent node representations. In the second step, we sort the sets of discrete latent representations and learn their distribution with a specifically designed auto-regressive model.
  arXiv  Detail & Related papers  (2023-06-13T12:40:39Z)
• Going Deeper into Permutation-Sensitive Graph Neural Networks [6.685139672294716]
  We devise an efficient permutation-sensitive aggregation mechanism via permutation groups. We prove that our approach is strictly more powerful than the 2-dimensional Weisfeiler-Lehman (2-WL) graph isomorphism test.
  arXiv  Detail & Related papers  (2022-05-28T08:22:10Z)
• 3D Equivariant Graph Implicit Functions [51.5559264447605]
  We introduce a novel family of graph implicit functions with equivariant layers that facilitates modeling fine local details. Our method improves over the existing rotation-equivariant implicit function from 0.69 to 0.89 on the ShapeNet reconstruction task.
  arXiv  Detail & Related papers  (2022-03-31T16:51:25Z)
• Gaussian-Hermite Moment Invariants of General Vector Functions to Rotation-Affine Transform [39.58178582162608]
  In this paper, we focus on constructing moment invariants of general vector functions. This is the first time that a uniform frame has been proposed in the literature to construct moment invariants. Based on synthetic and popular datasets of vector-valued data, the experiments are carried out to evaluate the stability and discriminability of these invariants.
  arXiv  Detail & Related papers  (2022-01-03T20:56:15Z)
• A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors [60.489902135153415]
  This paper introduces a variational iterative alternating scheme for hierarchical inverse problems with gamma hyperpriors. The proposed variational inference approach yields accurate reconstruction, provides meaningful uncertainty quantification, and is easy to implement.
  arXiv  Detail & Related papers  (2021-11-26T06:33:29Z)
• The general theory of permutation equivarant neural networks and higher order graph variational encoders [6.117371161379209]
  We derive formulae for general permutation equivariant layers, including the case where the layer acts on matrices by permuting their rows and columns simultaneously. This case arises naturally in graph learning and relation learning applications. We present a second order graph variational encoder, and show that the latent distribution of equivariant generative models must be exchangeable.
  arXiv  Detail & Related papers  (2020-04-08T13:29:56Z)
• Permutation Invariant Graph Generation via Score-Based Generative Modeling [114.12935776726606]
  We propose a permutation invariant approach to modeling graphs, using the recent framework of score-based generative modeling. In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph. For graph generation, we find that our learning approach achieves better or comparable results to existing models on benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-02T03:06:14Z)
• Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data [52.78581260260455]
  We propose a general method to construct a convolutional layer that is equivariant to transformations from any specified Lie group. We apply the same model architecture to images, ball-and-stick molecular data, and Hamiltonian dynamical systems.
  arXiv  Detail & Related papers  (2020-02-25T17:40:38Z)

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.