Learning to pre-train graph neural networks

Learning to pre-train graph neural networks

Graph neural networks (GNNs) have become the de facto standard for representation learning on graphs, which derive effective node representations by recursively aggregating information from graph neighborhoods. While GNNs can be trained from scratch, pre-training GNNs to learn transferable knowledge...

Saved in:

書目詳細資料
Main Authors:	LU, Yuanfu, JIANG, Xunqiang, FANG, Yuan, SHI, Chuan
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2021
主題:	pre-training graph neural networks meta-learning MAML Databases and Information Systems OS and Networks
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/6125 https://ink.library.smu.edu.sg/context/sis_research/article/7128/viewcontent/16552_Article_Text_20046_1_2_20210518.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

Contrastive pre-training of GNNs on heterogeneous graphs
由: JIANG, Xunqiang, et al.
出版: (2021)

Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
由: LIU, Zemin, et al.
出版: (2023)

Pre-training on large-scale heterogeneous graph
由: JIANG, Xunqiang, et al.
出版: (2021)

Generalizing graph neural network across graphs and time
由: WEN, Zhihao
出版: (2023)

Augmenting low-resource text classification with graph-grounded pre-training and prompting
由: WEN, Zhihao, et al.
出版: (2023)

Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
由: FENG FULI, et al.
出版: (2020)

Voucher abuse detection with prompt-based fine-tuning on graph neural networks
由: WEN, Zhihao, et al.
出版: (2023)

Dynamic meta-path guided temporal heterogeneous graph neural networks
由: JI, Yugang, et al.
出版: (2024)

Link prediction on latent heterogeneous graphs
由: NGUYEN, Trung Kien, et al.
出版: (2023)

Meta-learning on heterogeneous information networks for cold-start recommendation
由: LU, Yuanfu, et al.
出版: (2020)

Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs
由: YU, Xingtong, et al.
出版: (2024)

Pre-training graph neural networks for link prediction in biomedical networks
由: LONG, Yahui, et al.
出版: (2022)

Pruning meta-trained networks for on-device adaptation
由: GAO, Dawei, et al.
出版: (2021)

Social influence attentive neural network for friend-enhanced recommendation
由: LU, Yuanfu, et al.
出版: (2020)

Neighbor-anchoring adversarial graph neural networks
由: LIU, Zemin, et al.
出版: (2023)

Generalizing graph neural networks across graphs, time, and tasks
由: WEN, Zhihao
出版: (2023)

On size-oriented long-tailed graph classification of graph neural networks
由: LIU, Zemin, et al.
出版: (2022)

Neighbor-anchoring adversarial graph neural networks (extended abstract)
由: LIU, Zemin, et al.
出版: (2022)

Joint hyperbolic and Euclidean geometry contrastive graph neural networks
由: XU, Xiaoyu, et al.
出版: (2022)

On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach
由: CAI, Ruichu, et al.
出版: (2025)

Topic-aware heterogeneous graph neural network for link prediction
由: XU, Siyong, et al.
出版: (2021)

The 4th workshop on heterogeneous information network analysis and applications (HENA 2021)
由: SHI, Chuan, et al.
出版: (2021)

Forecasting interaction order on temporal graphs
由: XIA, Wenwen, et al.
出版: (2021)

Context-aware neural fault localization
由: ZHANG, Zhuo, et al.
出版: (2023)

Pre-training graph neural networks for link prediction in biomedical networks
由: Long, Yahui, et al.
出版: (2022)

DeepIS: Susceptibility estimation on social networks
由: XIA, Wenwen, et al.
出版: (2021)

Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers
由: CAI, Ruichu, et al.
出版: (2020)

TOWARD EFFECTIVE NEURAL NETWORKS ON GRAPH DATA
由: WANG YIWEI
出版: (2023)

Peer-inspired student performance prediction in interactive online question pools with graph neural network
由: LI, Haotian, et al.
出版: (2020)

Neighbor-anchoring adversarial graph neural networks
由: Liu, Zemin, et al.
出版: (2023)

Meta-inductive node classification across graphs
由: WEN, Zhihao, et al.
出版: (2021)

A graph theoretic approach for optimizing key pre-distribution in wireless sensor networks
由: Chan, A.C.-F.
出版: (2013)

Document graph representation learning
由: ZHANG, Ce
出版: (2023)

Multi-learner based recursive supervised training
由: IYER, Laxmi R., et al.
出版: (2006)

Node-wise localization of graph neural networks
由: LIU, Zemin, et al.
出版: (2021)

Explainable image recognition with graph-based feature extraction
由: Azam, Basim, et al.
出版: (2025)

Towards locality-aware meta-learning of tail node embeddings on networks
由: LIU, Zemin, et al.
出版: (2020)

Heterogeneous graph neural network with multi-view representation learning
由: SHAO, Zezhi, et al.
出版: (2023)

Motif graph neural network
由: CHEN, Xuexin, et al.
出版: (2023)

Efficient on-chip training of optical neural networks using genetic algorithm
由: Zhang, Hui, et al.
出版: (2021)