On size-oriented long-tailed graph classification of graph neural networks
The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multigraph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In partic...
Saved in:
Main Authors: | LIU, Zemin, MAO, Qiheng, LIU, Chenghao, FANG, Yuan, SUN, Jianling |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7489 https://ink.library.smu.edu.sg/context/sis_research/article/8492/viewcontent/TheWebConf22_SOLT.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Document graph representation learning
by: ZHANG, Ce
Published: (2023) -
Link prediction on latent heterogeneous graphs
by: NGUYEN, Trung Kien, et al.
Published: (2023) -
Forecasting interaction order on temporal graphs
by: XIA, Wenwen, et al.
Published: (2021) -
Hyperbolic graph topic modeling network with continuously updated topic tree
by: ZHANG, Ce, et al.
Published: (2023) -
Imbalanced graph classification with multi-scale oversampling graph neural networks
by: MA, Rongrong, et al.
Published: (2024)