[D] Is GNN or large graph model promising for an interpretable ...

Thanks for sharing your thoughts and the links to the Nature MI paper and your blog post. I believe that GNN and large graph models have great ...

A Fully Interpretable Graph Model Based on Large Language ... - arXiv

Traditional GNN methods focus on encoding the structural information of graphs, often using shallow text embeddings for node or edge attributes.

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

Data-driven surrogate modeling has surged in capability in recent years with the emergence of graph neural networks (GNNs), which can operate directly on ...

Verbalized Graph Representation Learning: A Fully Interpretable...

With the rise of large language models (LLMs), an increasing number of studies are combining them with GNNs for graph representation learning and downstream ...

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 ...

How Interpretable Are Interpretable Graph Neural Networks? - arXiv

We empirically validate our theoretical findings on a number of graph classification benchmarks. The results demonstrate that GMT outperforms ...

Pre-training Interpretable Graph Neural Networks - NIPS papers

In this papaer, we propose a Pre-training Interpretable Graph Neural Network model (π-GNN for short), which is first pre-trained over a large synthetic graph ...

Enhancing property and activity prediction and interpretation using ...

Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations ...

Explainable GNN-Based Models over Knowledge Graphs

MGNN transforms each pair of entities that co-occurs in a fact from the KG to a vertex in the encoded graph. However, the large number of ...

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

We denote the node feature matrix as X ∈ R|V|×d(0) where d(0) is the input feature dimension of each node. Graph Neural Network. We consider a GNN model that.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

The past few years have seen growing prevalence of using graph neural networks (GNN) for end-to-end graph learning applications. GNNs are the state-of-the-art ...

Explaining Graph Neural Networks Using Interpretable Local ...

We conduct experiments for explaining GNN models trained on both graph-level and node-level tasks. ... tives of the GNN function f : Rn×d → R (which we treat.

Local Interpretable Model Explanations for Graph Neural Networks

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

Interpretable temporal graph neural network for prognostic ...

To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while ...

Could graph neural networks learn better molecular representation ...

Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies ...

Graph Neural Network and Some of GNN Applications - neptune.ai

Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs.

(PDF) How Interpretable Are Interpretable Graph Neural Networks?

We empirically validate our theoretical findings on a number of graph classification benchmarks. The results demonstrate that GMT outperforms ...

Must-read papers on GNN - GitHub

... Large Graph Convolutional Networks. KDD 2019. paper ... Graph Classification. Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

The past few years have seen the growing prevalence of the use of graph neural networks (GNN) for end-to-end graph learning applications. GNNs ...

A review on graph neural networks for predicting synergistic drug ...

GNN-based models have demonstrated high performance and have yielded promising results in various aspects of drug discovery, including ...