Explainable graph classification with deep learning models
Graph Classification is a promising area of deep learning, but it has a significant drawback. We need to understand the reasons behind the model’s predicted label of an input graph to trust the prediction, but these reasons are not supplied by Graph Classification models. Hence, Graph Classification...
Saved in:
Main Author: | Rajiv Balamurugan |
---|---|
Other Authors: | Arijit Khan |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/148008 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Chromatic polynomials of signed graphs
by: Utomo, Charissa Irene
Published: (2023) -
Eigenvalues of the perfect matching derangement graph
by: Koh, Samuel Zhi Kang
Published: (2023) -
Toxicity prediction via algebraic graph-assisted bidirectional transformers
by: Ooi, Yen Sun
Published: (2023) -
Graphs with three eigenvalues
by: Xiong, Zhiyuan
Published: (2019) -
Deep learning for NTUQA forum question classification
by: Fung, Joseph King Yiu
Published: (2021)