Variational Graph Convolutional Neural Networks

• URL: http://arxiv.org/abs/2507.01699v1
• Date: Wed, 02 Jul 2025 13:28:37 GMT
• Title: Variational Graph Convolutional Neural Networks
• Authors: Illia Oleksiienko, Juho Kanniainen, Alexandros Iosifidis,
• Abstract summary: Uncertainty can help improve the explainability of Graph Convolutional Networks.<n>Uncertainty can also be used in critical applications to verify the results of the model.
• Score: 72.67088029389764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimation of model uncertainty can help improve the explainability of Graph Convolutional Networks and the accuracy of the models at the same time. Uncertainty can also be used in critical applications to verify the results of the model by an expert or additional models. In this paper, we propose Variational Neural Network versions of spatial and spatio-temporal Graph Convolutional Networks. We estimate uncertainty in both outputs and layer-wise attentions of the models, which has the potential for improving model explainability. We showcase the benefits of these models in the social trading analysis and the skeleton-based human action recognition tasks on the Finnish board membership, NTU-60, NTU-120 and Kinetics datasets, where we show improvement in model accuracy in addition to estimated model uncertainties.

Related papers

• Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming [13.180335574191432]
  This study developed a new graph neural network surrogate model named Recurrent U Net-based Graph Neural Network (RUGNN)<n>The RUGNN model can achieve accurate predictions of sheet material deformation fields across multiple forming timesteps.
  arXiv  Detail & Related papers  (2025-07-10T08:14:18Z)
• Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
  We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching.<n>We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy.<n>Our model outperforms existing models in both accuracy and inference time.
  arXiv  Detail & Related papers  (2024-11-02T07:06:53Z)
• Kolmogorov-Arnold Graph Neural Networks [2.4005219869876453]
  Graph neural networks (GNNs) excel in learning from network-like data but often lack interpretability.<n>We propose the Graph Kolmogorov-Arnold Network (GKAN) to enhance both accuracy and interpretability.
  arXiv  Detail & Related papers  (2024-06-26T13:54:59Z)
• A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks [0.0]
  We employ Bayesian neural networks to capture uncertainties in a reacting flow model. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.
  arXiv  Detail & Related papers  (2024-02-28T22:19:55Z)
• Human Trajectory Forecasting with Explainable Behavioral Uncertainty [63.62824628085961]
  Human trajectory forecasting helps to understand and predict human behaviors, enabling applications from social robots to self-driving cars. Model-free methods offer superior prediction accuracy but lack explainability, while model-based methods provide explainability but cannot predict well. We show that BNSP-SFM achieves up to a 50% improvement in prediction accuracy, compared with 11 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-04T16:45:21Z)
• NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration [66.22668336495175]
  A lack of consideration for neural network calibration will not gain trust from humans. We introduce the Neural Clamping Toolkit, the first open-source framework designed to help developers employ state-of-the-art model-agnostic calibrated models.
  arXiv  Detail & Related papers  (2022-11-29T15:03:05Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Sparse Flows: Pruning Continuous-depth Models [107.98191032466544]
  We show that pruning improves generalization for neural ODEs in generative modeling. We also show that pruning finds minimal and efficient neural ODE representations with up to 98% less parameters compared to the original network, without loss of accuracy.
  arXiv  Detail & Related papers  (2021-06-24T01:40:17Z)
• Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations [13.680949377743392]
  We address the problem of learning of a spreading model such that the predictions generated from this model are accurate. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach. We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model.
  arXiv  Detail & Related papers  (2020-07-13T17:58:21Z)
• Explainable Deep Modeling of Tabular Data using TableGraphNet [1.376408511310322]
  We propose a new architecture that produces explainable predictions in the form of additive feature attributions. We show that our explainable model attains the same level of performance as black box models.
  arXiv  Detail & Related papers  (2020-02-12T20:02:10Z)

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.