Graph Convolutional Network|Based Interpretable Machine ...

Graph Convolutional Network-Based Interpretable Machine ...

First, it utilizes time-series shapelet transform to extract key dynamics and convert the postfault time series into flat features. Second, it ...

A novel graph convolutional network-based interpretable method for ...

A novel graph convolutional network-based interpretable method for chiller energy consumption prediction considering the spatiotemporal coupling between ...

How Interpretable Are Interpretable Graph Neural Networks? - arXiv

Computer Science > Machine Learning. arXiv:2406.07955 (cs). [Submitted ... Existing XGNNs predominantly adopt the attention-based ...

GNNBook@2023: Interpretability in Graph Neural Networks

Interpretable machine learning, or explainable artificial intelligence, is experiencing rapid developments to tackle the opacity issue of deep learning ...

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

Abstract page for arXiv paper 2103.15587: IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction.

Graph Convolutional Network-Based Interpretable Machine ...

Therefore, in this article, a novel machine learning scheme based on a graph convolutional network is developed for SVS assessment. First, the time-series ...

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

This framework detects and diagnoses faults based on the propagation paths of different faults which are embedded into the architecture through graph ...

Deep Learning with Graph Convolutional Networks: An Overview ...

In the spectral convolutional neural network, such a layer structure transforms the features from p-dimensional to q-dimensional, and based on ...

Graph convolutional networks: a comprehensive review

First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph ...

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 ...

Explaining graph convolutional network predictions for clinicians ...

Interpretability for graph-based deep learning is even more challenging than ... Towards a rigorous science of interpretable machine learning. arXiv ...

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

Therefore, adopting interpretability in machine learning (ML) models is important, especially in healthcare [23]. Recently, the main efforts ...

An interpretable graph convolutional neural network based fault ...

This study proposed a fault diagnosis method based on interpretable graph neural network (GNN) suitable for building energy systems.

On the Explainability of Graph Convolutional Network With GCN ...

2.3 Neural Tangent Kernel. Based on the gaussian process (GP; Neal, 1995; Lee et al., 2018; de G. Matthews, Rowland, ...

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

... Interpretability of machine learning-based prediction models in healthcare. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery 10(5), e1379 ...

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 ...

Beyond Graph Convolutional Network: An Interpretable Regularizer ...

(Zhu et al. 2021) attempted to interpret existing GCN-based methods with a unified optimization framework, under which they de- vised an adjustable ...

Interpretable Graph Convolutional Neural Networks for Inference on ...

... based model on any graph-based machine learning task (node and graph classification, link prediction). Expand. 104 Citations. Add to Library. Alert.

Interpretable Graph Convolutional Network for Multi-View Semi ...

Crucially, based on a series of derivations, we propose a GCN-based framework with a flexible filter inspired by the optimization. Beyond that, a differentiable ...

Factorizable Graph Convolutional Networks

In the merging step, features from all latent graphs are concatenated to form the final features, which are block-wise interpretable. convolutional network ( ...