Pre-train Graph Neural Networks for Brain Network Analysis (Extended Abstract)-Reference-Cited by-同舟云学术

Pre-train Graph Neural Networks for Brain Network Analysis (Extended Abstract)

Author:

Yang Yi¹,Cui Hejie¹,Yang Carl¹

Affiliation:

1. Emory University,Department of Computer Science,Atlanta,GA,United States

Publisher

IEEE

Link

Reference14 articles.

1. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization;sun;ICLRE,2019

2. Progcl: Rethinking hard negative mining in graph contrastive learning;xia;ICML,2022

3. Transfer learning of graph neural networks with ego-graph information maximization;zhu;NeurIPS,2021

4. Gcc: Graph contrastive coding for graph neural network pretraining;qiu;SIGKDD,2020

5. Deep graph infomax;velickovic;ICLRE,2019

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3. BrainPST: Pre-training Stacked Transformers for Dynamic Brain Functional Network Analysis;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

4. Temporal Feature Mining in Dynamic Graph of Brain Connectivity Data;2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2023-10-01