GNNLens: A visual analytics approach for prediction error diagnosis of graph neural networks.
Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural...
Saved in:
Main Authors: | JIN, Zhihua, WANG, Yong, WANG, Qianwen, MING, Yao, MA, Tengfei, QU, Huamin |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2023
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7659 https://ink.library.smu.edu.sg/context/sis_research/article/8662/viewcontent/2011.11048.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
DeepDrawing: A deep learning approach to graph drawing
by: WANG, Yong, et al.
Published: (2020) -
Heterogeneous graph neural network with multi-view representation learning
by: SHAO, Zezhi, et al.
Published: (2023) -
GraphH: High performance big graph analytics in small clusters
by: SUN, Peng, et al.
Published: (2017) -
Constructing holistic spatio-temporal scene graph for video semantic role labeling
by: ZHAO, Yu, et al.
Published: (2023) -
AmbiguityVis: Visualization of ambiguity in graph layouts
by: WANG, Yong, et al.
Published: (2016)