Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

• URL: http://arxiv.org/abs/2110.14012v1
• Date: Tue, 26 Oct 2021 20:41:20 GMT
• Title: Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
• Authors: Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Z\"ugner, Stephan G\"unnemann
• Abstract summary: Uncertainty estimation for non-independent node-level predictions is under-explored. We propose a new model Graph Posterior Network (GPN) which explicitly performs Bayesian posterior updates for predictions on nodes. GPN outperforms existing approaches for uncertainty estimation in the experiments.
• Score: 37.86338466089894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNN). Nonetheless, uncertainty estimation for non-independent node-level predictions is under-explored. In this work, we explore uncertainty quantification for node classification in three ways: (1) We derive three axioms explicitly characterizing the expected predictive uncertainty behavior in homophilic attributed graphs. (2) We propose a new model Graph Posterior Network (GPN) which explicitly performs Bayesian posterior updates for predictions on interdependent nodes. GPN provably obeys the proposed axioms. (3) We extensively evaluate GPN and a strong set of baselines on semi-supervised node classification including detection of anomalous features, and detection of left-out classes. GPN outperforms existing approaches for uncertainty estimation in the experiments.

Related papers

• CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks [21.602569813024]
  We study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. We propose a family of Committe-based Uncertainty Quantification Graph Neural Networks (CUQ-GNNs)
  arXiv  Detail & Related papers  (2024-09-06T09:43:09Z)
• Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing [4.184419714263417]
  We reevaluating the concept of neighborhood aggregation, which is a fundamental component in graph neural networks (GNNs) Our analysis reveals conceptual flaws within certain benchmark GNN models when operating under the assumption of edge-independent node labels.
  arXiv  Detail & Related papers  (2024-07-21T22:37:24Z)
• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels [81.93520935479984]
  We study a new problem, GNN model evaluation, that aims to assess the performance of a specific GNN model trained on labeled and observed graphs. We propose a two-stage GNN model evaluation framework, including (1) DiscGraph set construction and (2) GNNEvaluator training and inference. Under the effective training supervision from the DiscGraph set, GNNEvaluator learns to precisely estimate node classification accuracy of the to-be-evaluated GNN model.
  arXiv  Detail & Related papers  (2023-10-23T05:51:59Z)
• Uncertainty Propagation in Node Classification [9.03984964980373]
  We focus on measuring uncertainty of graph neural networks (GNNs) for the task of node classification. We propose a Bayesian uncertainty propagation (BUP) method, which embeds GNNs in a Bayesian modeling framework. We present an uncertainty oriented loss for node classification that allows the GNNs to clearly integrate predictive uncertainty in learning procedure.
  arXiv  Detail & Related papers  (2023-04-03T12:18:23Z)
• A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs [49.76175970328538]
  We propose novel edgewise metrics, namely the edgewise expected calibration error (ECE) and the agree/disagree ECEs, which provide criteria for uncertainty estimation on graphs beyond the nodewise setting. Our experiments demonstrate that the proposed edgewise metrics can complement the nodewise results and yield additional insights.
  arXiv  Detail & Related papers  (2022-10-27T16:12:58Z)
• Debiased Graph Neural Networks with Agnostic Label Selection Bias [59.61301255860836]
  Most existing Graph Neural Networks (GNNs) are proposed without considering the selection bias in data. We propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer. Our proposed model outperforms the state-of-the-art methods and DGNN is a flexible framework to enhance existing GNNs.
  arXiv  Detail & Related papers  (2022-01-19T16:50:29Z)
• Uncertainty Aware Semi-Supervised Learning on Graph Data [18.695343563823798]
  We propose a multi-source uncertainty framework using a graph neural network (GNN) for node classification predictions. By collecting evidence from the labels of training nodes, the Graph-based Kernel Dirichlet distribution Estimation (GKDE) method is designed for accurately predicting node-level Dirichlet distributions. We found that dissonance-based detection yielded the best results on misclassification detection while vacuity-based detection was the best for OOD detection.
  arXiv  Detail & Related papers  (2020-10-24T04:56:46Z)
• Bilinear Graph Neural Network with Neighbor Interactions [106.80781016591577]
  Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
  arXiv  Detail & Related papers  (2020-02-10T06:43:38Z)

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.