- A Demonstration of Interpretability Methods for Graph Neural Networks🔍
- Interpretability Methods for Graph Neural Networks🔍
- A graph|based interpretability method for deep neural networks🔍
- flyingdoog/awesome|graph|explainability|papers🔍
- Interpretability in Graph Neural Networks🔍
- How Interpretable Are Interpretable Graph Neural Networks?🔍
- GNNExplainer🔍
- Interpretability of Graph Neural Networks🔍
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 ...