Graph neural networks on factor graphs for robust

Graph Neural Networks on Factor Graphs for Robust, Fast ... - arXiv

We present a method that uses graph neural networks (GNNs) to learn complex bus voltage estimates from PMU voltage and current measurements.

Graph neural networks on factor graphs for robust, fast, and scalable ...

We present a method that uses graph neural networks (GNNs) to learn complex bus voltage estimates from PMU voltage and current measurements.

Graph neural networks on factor graphs for robust, fast, and scalable ...

Furthermore, we augment the factor graph to improve the robustness of GNN predictions. This model is highly efficient and scalable, as its computational ...

Graph neural networks on factor graphs for robust, fast, and scalable ...

Graph neural networks on factor graphs for robust, fast, and scalable linear state estimation with PMUs · List of references · Publications that cite this ...

Adversarial Robustness - Graph Neural Networks

In contrast to other application domains of deep learning, robustness analysis for graphs is especially challenging for multiple reasons: 1. Complex ...

Factor Graph Neural Networks

Furthermore, inference algorithms for many types of graphs, e.g., graphs with typed edges or nodes, are easily developed using the factor graph representation.

[2308.00887] Factor Graph Neural Networks - arXiv

More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order ...

Reliable Graph Neural Networks via Robust Aggregation

Equipping a GNN with our aggregation improves the robustness with respect to structure perturbations on Cora ML by a factor of 3 (and 5.5 on Citeseer) and by a ...

robust graph neural networks via adaptive framelet convolution

On graphs, many state-of-the-art GNN models follow the same idea and adopt -based graph smoothing when recasting them as solving a graph signal ...

Robust Graph Neural Networks via Unbiased Aggregation

The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks ...

Factor Graph Neural Networks - Review for NeurIPS paper

Summary and Contributions: 1. the paper proposes a neural network on factor graphs for MAP inference; 2. the paper proves that the max-product algorithm is a ...

Factor Graphs for Robot Perception

... graph” rather than factor graph) provide the option of using these robust error functions in the factors. Commonly used functions include the Huber and ...

GraphSHINE: Training Shift-Robust Graph Neural Networks with...

Graph neural networks (GNNs) have achieved remarkable performance across predictive tasks on graph-structured data.

Reduced-Rank Topology Learning for Robust and Scalable Graph ...

Deng et al., GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks. Proceedings of the First Learning on Graphs ...

Factor graph neural networks | The Journal of Machine Learning ...

In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an ...

Graph Structure Learning for Robust Graph Neural Networks

The GNNs have proven to be effective in analyzing such graph-structured data [36] . Therefore, we propose employing graph representations to capture the ...

Neural Enhanced Belief Propagation on Factor Graphs

As a result, we obtain a more ac- curate algorithm that combines the benefits of both belief propagation and graph neural networks. We apply our ideas to error ...

Pushing Factor Graphs beyond SLAM (Frank Dellaert) - YouTube

Factor Graphs and Robust Perception | Michael Kaess | Tartan SLAM Series ... Zachary Teed - Optimization Inspired Neural Networks for Multiview 3D.

[D] Why I'm Lukewarm on Graph Neural Networks - Reddit

As noted in the OpenGraphsBenchmark (OGB) paper, GNN papers do their empirical section on a handful of tiny graphs (Cora, CiteSeer, PubMed) with ...

RSGNN: A Model-agnostic Approach for Enhancing the Robustness ...

Signed graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the vulnerability of SGNN to potential edge noise in ...