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

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 ...

Gradient-Based Interpretable Graph Convolutional Network for ...

A gradient-based interpretable graph convolutional network (GIGCN) is proposed for bearing fault diagnosis, which analyzes the interpretability of the fault ...

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

Above results demonstrated the power of DSB-GNN to capture relationships between the 3D chromatin structure and DSBs, allowing for further ...

Graph Convolutional Network-Based Interpretable Machine ...

Second, it designs a graph convolutional network (GCN) to incorporate these features with topology information for SVS assessment. The GCN ...

DRExplainer: Quantifiable Interpretability in Drug Response ... - arXiv

... Interpretability in Drug Response Prediction with Directed Graph Convolutional Network. ... methods and another graph-based explanation method ...

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.

Interpretable Graph Convolutional Neural Networks for Inference on ...

This work proposes a novel multi-level graph neural network (M-GNN), which ... based model on any graph-based machine learning task. Expand. 1,115 ...

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 fashion. In this ...

A Biologically Interpretable Graph Convolutional Network to Link ...

In this paper, we introduce an interpretable Genetics and mUltimodal Imaging based DEep neural network (GUIDE), for whole-brain and whole-genome ...

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 ...

Dual-graph Learning Convolutional Networks for Interpretable ...

Authors. Tingsong Xiao, Lu Zeng, Xiaoshuang Shi, Xiaofeng Zhu, Guorong Wu. Abstract. In this paper, we propose a dual-graph learning convolutional network ...

Factorizable Graph Convolutional Networks

In this paper, we introduce a novel graph convolutional network (GCN), termed as ... For the other three graph datasets, we add non DL-based methods (WL.

Graph Neural Networks and Their Current Applications in ... - NCBI

Two modeling methods of biological data with graph structure. (A) Molecular structure-based modeling. (B) Biological network-based modeling. The ...

(PDF) A Biologically Interpretable Graph Convolutional Network to ...

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

Beyond Graph Convolutional Network: An Interpretable Regularizer ...

2021) attempted to interpret existing GCN-based methods with a unified optimization framework, under which they de- vised an adjustable graph filter for a new ...

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 convolution and pooling ...

Explaining decisions of graph convolutional neural networks: patient ...

... interpretable insights. On ... Interpreting and understanding graph convolutional neural network using gradient-based attribution method.

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

An interpretable attention module (IAM) that explains the relevance of the input features to the classification task on a GNN Model and uses these ...

Interpretable and Efficient Heterogeneous Graph Convolutional ...

graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot ...

NHGNN-DTA: a node-adaptive hybrid graph neural network for ...

... based DTA predictions and interpretability. During this process, the ... Compared to sequence-based methods, graph-based methods focus on building ...