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A Demonstration of Interpretability Methods for Graph Neural Networks


A Demonstration of Interpretability Methods for Graph Neural Networks

Computing methodologies → Machine learning approaches; •. Information systems → Network data models. KEYWORDS. Graph neural network, interpretability, ...

A Demonstration of Interpretability Methods for Graph Neural Networks

This paper demonstrates gInterpreter with an interactive performance profiling of 15 recent GNN inter-pretability methods, aiming to explain the complex deep ...

A Demonstration of Interpretability Methods for Graph Neural Networks

An end-to-end interactive tool, named gInterpreter, is developed by re-implementing 15 recent GNN interpretability methods in a common environment on top of ...

A Demonstration of Interpretability Methods for Graph Neural Networks

For an image, benign or adversarial, we study how a neural network's architecture can induce an associated graph. We study this graph and introduce specific ...

A Demonstration of Interpretability Methods for Graph Neural Networks

AB - Graph neural networks (GNNs) are widely used in many downstreamapplications, such as graphs and nodes classification, entity resolution, ...

Tutorial: Interpretability Methods for Graph Neural Networks

The emerging graph neural network models (GNNs) have demonstrated great potential and success for downstream graph machine learning tasks, such as graph and ...

A Demonstration of Interpretability Methods for Graph Neural Networks

A Demonstration of Interpretability Methods for Graph Neural Networks. 103 views · 1 year ago ...more. Try YouTube Kids. An app made just for ...

A Demonstration of Interpretability Methods for Graph Neural Networks

Explainability Techniques for Graph Convolutional Networks. In ICML Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations. L.

Interpretability Methods for Graph Neural Networks - IEEE Xplore

The emerging graph neural network models (GNNs) have demonstrated great potential and success for downstream graph machine learning tasks, such as graph and ...

A graph-based interpretability method for deep neural networks

The experimental results show that the proposed method can interpret the associations among the weight parameters as well as the correlation ...

flyingdoog/awesome-graph-explainability-papers - GitHub

[GRADES & NDA'23] A Demonstration of Interpretability Methods for Graph Neural Networks [paper]; [Arxiv 23] Self-Explainable Graph Neural Networks for Link ...

Interpretability in Graph Neural Networks

definition of interpretability/interpretation, the reasons for exploring model inter- pretation, the methods of obtaining interpretation in traditional deep ...

How Interpretable Are Interpretable Graph Neural Networks? - arXiv

... interpretable subgraph. However, the representational properties and limitations of these methods remain inadequately explored. In this work ...

GNNExplainer: Generating Explanations for Graph Neural Networks

At a high level, we can group those interpretability methods for non-graph neural networks into two main families. ... Results show that GNNEXPLAINER outperforms ...

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' ...

How Interpretable Are Interpretable Graph Neural Networks?

Results on geometric graphs. Tables 3 and 4 show the interpretation and generalization performances of various methods. Again, we observe consistent non ...

[2306.01958] A Survey on Explainability of Graph Neural Networks

... methods, identifying gaps, and fostering further advancements in interpretable graph-based machine learning. Comments: submitted to Bulletin ...

Pre-training Interpretable Graph Neural Networks - NIPS papers

Recently graph representation learning based pre-training methods have been investigated [22, 21, 23, 24], however, they target at downstream prediction tasks ...

Graph neural networks: A review of methods and applications

Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs.

Logic-based interpretability of Graph Neural Networks by ... - YouTube

... ways to explain and interpret these models is an active area of research. The increase in interest on the topic has yielded several new methods ...