Factor Graph Neural Networks

[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 ...

We generalize the GNN into a factor graph neural network (FGNN) providing a simple way to incorporate dependencies among multiple variables.

Factor Graph Neural Networks

We propose Factor Graph Neural Networks (FGNNs) to effectively capture higher-order relations for inference and learning. To do so, we first derive an efficient ...

[1906.00554] Factor Graph Neural Network - arXiv

We generalize the graph neural network into a factor graph neural network (FGNN) in order to capture higher order dependencies.

zzhang1987/Factor-Graph-Neural-Network - GitHub

This repo provides the code for testing FGNN on synthetic MAP inference problem and point cloud segmentation on real dataset.

Factor Graph Neural Network

We take the approach of jointly learning the inference algorithm and latent variables in developing the factor graph neural network (FGNN). The FGNN is defined ...

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.

Neural Enhanced Belief Propagation on Factor Graphs

In this work we first extend graph neural networks to factor graphs (FG-GNN). We then propose a new hybrid model that runs conjointly a FG-GNN with belief ...

Factor graph neural network | Proceedings of the 34th International ...

These networks often only consider pairwise dependencies, as they operate on a graph structure. We generalize the GNN into a factor graph neural network (FGNN) ...

[PDF] Factor Graph Neural Networks | Semantic Scholar

Factor Graph Neural Networks · Zhen Zhang, Mohammed Haroon Dupty, +1 author. Fan Wu · Published in Journal of machine learning… 2 August 2023 · Computer Science, ...

Invariant Factor Graph Neural Networks - IEEE Xplore

Though several attempts have been made to deal with the issue, they mainly focus on structural properties while overlooking rich graph feature information. To ...

Neural Enhanced Belief Propagation on Factor Graphs

in the factor graph, leading to suboptimal es- timates. In this work we first extend graph neural networks to factor graphs (FG-GNN). We then propose a new ...

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 ...

What are "Factor Graphs" and what are they useful for?

A factor graph is a bipartite graph with both factor nodes and variable nodes. ... Neural network do it better. And Factor Graph is exactly solve ...

Factor Graph Neural Network - NUS Computing

These networks often only consider pairwise dependencies, as they operate on a graph structure. We generalize the GNN into a factor graph neural network (FGNN) ...

[PDF] Factor Graph Neural Network | Semantic Scholar

FGNN is shown to represent Max-Product Belief Propagation, an approximate inference algorithm on probabilistic graphical models; ...

(PDF) Factor Graph Neural Networks - ResearchGate

We propose Factor Graph Neural Networks (FGNNs) to effectively capture higher-order relations for inference and learning. To do so, we first ...

Factor Graph Neural Network - Singapore - ScholarBank@NUS

Zhen Zhang, Fan Wu, Wee Sun Lee (2019-06-03). Factor Graph Neural Network. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). ScholarBank@NUS ...

Factor Graph-based Interpretable Neural Networks | OpenReview

We propose AGAIN, a fActor GrAph-based Interpretable neural Network, which is capable of generating comprehensible explanations under unknown perturbations.

BeautyOfWeb/FactorGraphNeuralNet: The Factor Graph Neural ...

To address this challenge, we developed the Factor Graph Neural Network model that is interpretable and predictable by combining probabilistic graphical models ...