Gradient|Based Interpretable Graph Convolutional Network for ...

Gradient-Based Interpretable Graph Convolutional Network for ...

To deal with such a problem, a gradient-based interpretable graph convolutional network (GIGCN) is proposed for bearing fault diagnosis, which analyzes the ...

Gradient-Based Interpretable Graph Convolutional Network for ...

Gradient-Based Interpretable Graph Convolutional. Network for Bearing Fault Diagnosis. Kairu Wen. Shien-Ming Wu School of Intelligent. Engineering. South China ...

Gradient-Based Interpretable Graph Convolutional Network for ...

Request PDF | On May 22, 2023, Kairu Wen and others published Gradient-Based Interpretable Graph Convolutional Network for Bearing Fault Diagnosis | Find, ...

How Interpretable Are Interpretable Graph Neural Networks? - arXiv

Existing XGNNs predominantly adopt the attention-based mechanism to learn edge or node importance for extracting and making predictions with the ...

A graph neural network-based interpretable framework reveals a ...

In DSB-GNN, Hi-C contact map is converted into graph, of which the nodes and edges represent the genome regions and chromatin interactions. The ...

HennyJie/IBGNN: MICCAI 2022 (Oral): Interpretable Graph Neural ...

MICCAI 2022 (Oral): Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis - HennyJie/IBGNN.

A graph-based interpretability method for deep neural networks

Graph is used to describe parameters relationship between layers and within layers. By changing the data format of the parameters, the ...

An interpretable graph convolutional neural network based fault ...

The diagnosis results show that the proposed method has a diagnosis accuracy of over 96%. The interpretation results show that the method is ...

IA-GCN: Interpretable Attention based Graph Convolutional Network ...

Title:IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction ... Abstract:Interpretability in Graph ...

IA-GCN: Interpretable Attention based Graph Convolutional Network ...

Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in general in computer vision; yet, in the medical ...

GNNBook@2023: Interpretability in Graph Neural Networks

Opportunities and Challenges in GNN Interpretability. Explanation Methods for Graph Neural Networks. Background; Approximation-Based Explanation; Relevance ...

Graph neural networks for an accurate and interpretable prediction ...

Here, we develop a graph neural network based machine learning model which enables an accurate prediction of the property of ...

Context-Based Interpretable Spatio-Temporal Graph Convolutional ...

Context-based Interpretable Spatio-Temporal Graph Convolutional Network for. Human Motion Forecasting. Edgar Medina, Leyong Loh, Namrata Gurung, Kyung Hun Oh ...

GNNExplainer: Generating Explanations for Graph Neural Networks

Figure 1: GNNEXPLAINER provides interpretable explanations for predictions made by any GNN model on any graph-based machine learning task. Shown is a ...

Interpretable Graph Convolutional Neural Networks for Inference on ...

Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs ... Graph Convolutional Neural Network ... based model on any graph-based ...

A propagation path-based interpretable neural network model for ...

One method of embedding graph structures into neural networks is through a deep learning model called a Graph Convolutional Network (GCN) (Kipf & Welling, 2016) ...

Explaining graph convolutional network predictions for clinicians ...

Interpretability for graph-based deep learning is even more challenging than CNN or Recurrent Neural Network (RNN) based models since graph nodes and edges ...

IA-GCN: Interpretable Attention Based Graph Convolutional Network ...

Most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the output of the model in a post-hoc ...

KerGNNs: Interpretable Graph Neural Networks with Graph Kernels

Most GNNs are based on Message Passing Neu- ral Network (MPNN) frameworks. However, recent studies show that MPNNs can not exceed the power of the Weisfeiler-.

A Biologically Interpretable Graph Convolutional Network to Link...

We propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph ...