Pre|training Interpretable Graph Neural Networks

Pre-training Interpretable Graph Neural Networks - NIPS papers

Motivated by the great success of recent pre-training techniques, we for the first time propose the. Pre-training Interpretable Graph Neural Network (π-GNN3) to ...

Pre-training Interpretable Graph Neural Networks | OpenReview

The key innovation of this method is that of relying on synthetic graphs with known explanations to pretrain the model. The pretraining helps to learn general ...

Pre-training Interpretable Graph Neural Networks

We for the first time propose the Pre-training Interpretable Graph Neural Network (π π -GNN) to distill the universal interpretability of GNNs by pre-training ...

pre-training interpretable graph neural networks - ACM Digital Library

Motivated by the great success of recent pre-training techniques, we for the first time propose the Pre-training Interpretable Graph Neural ...

How Interpretable Are Interpretable Graph Neural Networks? - arXiv

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

Graph neural networks for an accurate and interpretable prediction ...

Such a microstructure-graph-based GNN model, therefore, enables an accurate and interpretable prediction of the properties of polycrystalline ...

Interpretable Graph Neural Networks for Heterogeneous Tabular Data

Subsequently, we outline the training process of the proposed method as a straightforward GNN for graph classification. Finally, we describe how ...

[NeurIPS2023] Learning on Graphs - GitHub

[NeurIPS2023] Learning on Graphs ; Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks, here, Jun Yin, Chaozhuo Li, Hao Yan, ...

KerGNNs: Interpretable Graph Neural Networks with Graph Kernels

specific graph, and can be helpful for interpreting the pre- dictions of ... Pre- training graph neural networks with kernels. arXiv preprint. arXiv ...

Towards Interpretable Graph Neural Networks - ACM Digital Library

Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks. In Annual Conference on Neural Information Processing ...

How Interpretable Are Interpretable Graph Neural Networks?

Pre-training of deep bidirectional transformers for lan- guage understanding. In Conference of the North Amer- ican Chapter of the Association for ...

Towards interpretable graph neural networks for transport prediction ...

Therefore, we pre-train the encoder for 30 epochs using only the KL divergence part of the loss. This means that when the model starts full training, the ...

Interpretability in Graph Neural Networks

ing training data. The model will behave normally unless it is ... Relevance propagation redistributes the activation score of output neuron to its pre-.

flyingdoog/awesome-graph-explainability-papers - GitHub

[ICML 24] How Interpretable Are Interpretable Graph Neural Networks? ... [NeurIPS 23] Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural ...

Interpretable and Generalizable Graph Learning via Stochastic ...

Previous works mostly focused on using post- hoc approaches to interpret pre-trained models. (graph neural networks in particular). They ar- gue against ...

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 ...

L2XGNN: learning to explain graph neural networks

In contrast to post-hoc methods, approaches with built-in interpretability provide explanations during training by introducing new mechanisms, ...

Interpretable temporal graph neural network for prognostic ...

“Graph attention networks,” in Int Conf on Learn Represent (ICLR), 2018. [Google Scholar]; [10]. Hu W et al. , “Strategies for pre-training graph neural ...

Efficient and interpretable graph network representation for angle ...

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and ...

Interpretable A-posteriori error indication for graph neural network ...

Physics-informed neural networks leverage known partial differential equations (PDEs) to augment standard supervised training objective functions, which in turn ...