Imbalanced graph classification with multi-scale oversampling graph neural networks

Imbalanced graph classification with multi-scale oversampling graph neural networks

One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and imbalanced learning loss functions, can be adopted for enabling graph representat...

Saved in:

書目詳細資料
Main Authors:	MA, Rongrong, PANG, Guansong, CHEN, Ling
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2024
主題:	Graph classification Imbalanced learning Oversampling Graph neural networks Computer Sciences Graphics and Human Computer Interfaces
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/9764 https://ink.library.smu.edu.sg/context/sis_research/article/10764/viewcontent/2405.04903v2.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

相似書籍

A class-aware representation refinement framework for graph classification
由: Xu, Jiaxing, et al.
出版: (2024)

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

Deep graph-level anomaly detection by glocal knowledge distillation
由: MA, Rongrong, et al.
出版: (2022)

Harnessing collective structure knowledge in data augmentation for graph neural networks
由: MA, Rongrong, et al.
出版: (2024)

Graph neural point process for temporal interaction prediction
由: XIA, Wenwen, et al.
出版: (2023)

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

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

Contrastive graph pooling for explainable classification of brain networks
由: Xu, Jiaxing, et al.
出版: (2024)

Deep learning for graph structured data
由: Dwivedi Vijay Prakash
出版: (2024)

Learning and Reasoning on Graph for Recommendation
由: Xiang Wang, et al.
出版: (2020)

Learning and Reasoning on Graph for Recommendations
由: Xiang Wang, et al.
出版: (2020)

Graph contrastive learning with stable and scalable spectral encoding
由: BO, Deyu, et al.
出版: (2023)

Empirical evaluation of minority oversampling techniques in the context of Android malware detection
由: SHAR, Lwin Khin, et al.
出版: (2021)

Iterative graph self-distillation
由: ZHANG, Hanlin, et al.
出版: (2024)

Prototypical graph contrastive learning
由: LIN, Shuai, et al.
出版: (2022)

DiffSeer: Difference-based dynamic weighted graph visualization
由: WEN, Xiaolin, et al.
出版: (2023)

GNNLens: A visual analytics approach for prediction error diagnosis of graph neural networks.
由: JIN, Zhihua, et al.
出版: (2023)

Towards effective graph representations by leveraging geometric concepts
由: Lee, See Hian
出版: (2024)

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

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

Perfection of glued graphs of perfect original graphs
由: Supaporn Saduakdee
出版: (2011)

Perfection of glued graphs of perfect original graphs
由: Supaporn Saduakdee
出版: (2008)

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

TOWARD EFFECTIVE AND EFFICIENT GRAPH NEURAL NETWORKS WITH IMPLICIT LAYERS
由: LIU JUNCHENG
出版: (2024)

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

Imbalanced text classification: A term weighting approach
由: Liu, Y., et al.
出版: (2014)

Graph-level anomaly detection via hierarchical memory networks
由: NIU, Chaoxi, et al.
出版: (2023)

Constructing unit-distance graphs by subdividing edges
由: Calayag, Ernita Rodriguez
出版: (2002)

Total colorings of glued graphs
由: Wongsakorn Charoenpanitseri
出版: (2013)

Total colorings of glued graphs
由: Wongsakorn Charoenpanitseri
出版: (2007)

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

Exploring & exploiting high-order graph structure for sparse knowledge graph completion
由: HE, Tao, et al.
出版: (2025)

On the fold thickness of some classes of graphs
由: Campena, Francis Joseph H.
出版: (2006)

ALGORITHMIC INDUCTIVE BIASES FOR GRAPH REPRESENTATION LEARNING
由: MOHAMMED HAROON DUPTY
出版: (2022)

ADVANCING GRAPH NEURAL NETWORKS WITH HL-HGAT: A HODGE-LAPLACIAN AND ATTENTION MECHANISM APPROACH FOR HETEROGENEOUS GRAPH-STRUCTURED DATA
由: HUANG JINGHAN
出版: (2024)

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

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

Modeling functional similarity in source code with graph-based Siamese networks
由: MEHROTRA, Nikita, et al.
出版: (2022)

Heterogeneous graph transformer with poly-tokenization
由: LU, Zhiyuan, et al.
出版: (2024)

Contextualized graph attention network for recommendation with item knowledge graph
由: Liu, Yong, et al.
出版: (2022)