GNNExplainer: generating explanations for graph neural networks. in Advances in Neural Information Processing Systems (ed. Wallach, H. et al.) Vol. 32 (Curran Associates, Inc., 2019). Agarwal, C., Queen, O., Lakkaraju, H. & Zitnik, M. Evaluating explainability for graph neural networks....
Graph neural networks (GNN) are deep learning models that leverage spatial information inherent in the dataset for inference. Here, we develop an MRI cortical thickness (CT) based brain age prediction framework using GNNs and illustrate that it provides a feasible mechanism to identify contributing ...
temporal, central, and occipital brain regions by using two groups. In most of the brain regions, significant differences were found in the AD group compared to the NC group. Zhou et al. [23] classified AD as a graph classification task using brain point data. Second...
For each local cell graph, we defined the neighbourhood ageing as the average age acceleration of all cells in the graph. We defined cell (node) features as a one-hot vector for cell type using the 18 cell-type annotations. We trained a GNN model using PyTorch Geometric108 to predict ...
For each local cell graph, we defined the neighbourhood ageing as the average age acceleration of all cells in the graph. We defined cell (node) features as a one-hot vector for cell type using the 18 cell-type annotations. We trained a GNN model using PyTorch Geometric108 to predict ...
Ying, Z., Bourgeois, D., You, J., Zitnik, M. & Leskovec, J. GNNExplainer: generating explanations for graph neural networks. inAdvances in Neural Information Processing Systems(ed. Wallach, H. et al.) Vol. 32 (Curran Associates, Inc., 2019). ...
A Graph Attention Neural Network for Diagnosing ASD with fMRI DataBIBM2021-- BrainMixup: Data Augmentation for GNN-based Functional Brain Network AnalysisBig Data2022-- BRAIN NETWORK TRANSFORMERNeurIPS2022BRAINNETTF[Code] BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI DatasetBIBM2022-- ...
2022 FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation MIDL 2022 Link Link 2022 BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks TMI 2022 Link Link These publications offer a range of approaches and tools for those interested in Brain ...
learning long-range relationships,our method, which we call GraphTrans, applies a permutation-invariantTransformer module after a standard GNN module. This simple architecture leadsto state-of-the-art results on several graph classification tasks,outperforming methods that explicitly encode graph structure...
wherehGis the representation of graphG. In this study, we use sum-pooling (Xu et al., 2018hG=SUMzi|i∈G. 2.1.2. GNN interpretability Although GNNs have shown remarkable effectiveness, they are black-box models that lack interpretability, making it difficult to understand the underlying ...