Interpretability of Graph Neural Networks

Interpretability of Graph Neural Networks - YouTube

MASTER Workshop August 11th, 2023 -- RDC 511 / PUC-Rio Interpretability of Graph Neural Networks Alessandro Bicciato (Unive - Università Ca' ...

Local Interpretable Model Explanations for Graph Neural Networks

Index Terms—Graph neural networks, interpretability, explanation. ا. 1 INTRODUCTION. Deep Neural Network (DNN) is essentially a new machine learn- ing ...

Could graph neural networks learn better molecular representation ...

Graph neural networks (GNN) has been ... Graph neural networks ... interpretability to the highly complicated and specialized graph-based DL models.

The Interpretability of Graph Neural Networks - Talks

Graph neural networks (GNNs) have demonstrated great performance on graph-based data. However, like many machine learning models, GNNs lack ...

Self-Interpretable Graph Learning with Sufficient and Necessary ...

Graph Neural Networks (GNNs) are widely employed for learning representations of graph-structured data, such as social networks (Xiao et al. 2023), co-purchase ...

Towards Scalable and Interpretable Graph Neural Networks

This project proposes novel principles and mechanisms for scalable and interpretable graph neural networks to facilitate the adoption of GNNs on critical ...

Scalability and interpretability of graph neural networks for small ...

I propose a novel kernel-inspired graph neural network architecture, called a subgraph matching neural network (SMNN), which is designed to have all feature ...

Does Captum support Graph Neural Networks (GNN)?

Hello everybody. I have recently started using Captum for model interpretability and I've found it really interesting.

GraphLIME: Local Interpretable Model Explanations for Graph ...

Recently, graph neural networks (GNN) were shown to be successful in effectively representing graph structured data because of their good performance and ...

HCat-GNet: An Interpretable Graph Neural Network for Catalysis ...

To overcome this, we propose a homogeneous catalyst graph neural network (HCat-GNet) for the prediction of selectivity of catalysts given the ...

Polymer-Unit Graph: Advancing Interpretability in Graph Neural ...

The graph representation of complex materials plays a crucial role in the field of inorganic and organic materials investigations for ...

GNNExplainer: Generating Explanations for Graph Neural Networks

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively ...

How Interpretable Are Interpretable Graph Neural Networks?

Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research.

TOWARD ROBUST AND INTERPRETABLE GRAPH AND IMAGE ...

Lastly, we studied the interpretability of graph neural networks. We developed a self-interpretable GNN structure that denoises useless ...

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 ...

On The Interpretability of Graph Neural Networks in QSPR Modeling

On The Interpretability of Graph Neural Networks in QSPR Modeling. https://doi.org/10.1016/b978-0-323-95879-0.50233-2. Journal: Computer Aided Chemical ...

On The Interpretability of Graph Neural Networks in QSPR Modeling

Abstract ... However, their 'black box' nature and the lack of transparency and interpretability could hinder their wider acceptance and usage. In ...

Theory of Graph Neural Networks: Representation and Learning

Join the Learning on Graphs and Geometry Reading Group: https://hannes-stark.com/logag-reading-group Paper “Theory of Graph Neural Networks: ...

Interpretable Graph Neural Networks for Connectome-Based Brain ...

Keywords: Interpretation · Graph neural network · Brain networks. 1 Introduction. Brain networks (a.k.a the connectome) are complex graphs with anatomic regions.