Interpretability Methods for Graph Neural Networks

Arijit Khan; Ehsan B. Mobaraki

doi:10.1109/DSAA60987.2023.10302600

Interpretability Methods for Graph Neural Networks

Arijit Khan
, Ehsan B. Mobaraki

Aalborg University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
Editors	Yannis Manolopoulos, Zhi-Hua Zhou
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350345032
DOIs	https://doi.org/10.1109/DSAA60987.2023.10302600
State	Published - 2023
Externally published	Yes
Event	10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 - Thessaloniki, Greece Duration: Oct 9 2023 → Oct 12 2023

Publication series

Name	2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings

Conference

Conference	10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023
Country/Territory	Greece
City	Thessaloniki
Period	10/9/23 → 10/12/23

ASJC Scopus Subject Areas

Information Systems and Management
Statistics, Probability and Uncertainty
Artificial Intelligence
Computer Science Applications
Information Systems

Keywords

explainable AI
graph neural networks
interpretability

Access to Document

10.1109/DSAA60987.2023.10302600

Cite this

Khan, A., & Mobaraki, E. B. (2023). Interpretability Methods for Graph Neural Networks. In Y. Manolopoulos, & Z.-H. Zhou (Eds.), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA60987.2023.10302600

Interpretability Methods for Graph Neural Networks. / Khan, Arijit; Mobaraki, Ehsan B.
2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. ed. / Yannis Manolopoulos; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Khan, A & Mobaraki, EB 2023, Interpretability Methods for Graph Neural Networks. in Y Manolopoulos & Z-H Zhou (eds), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023, Thessaloniki, Greece, 10/9/23. https://doi.org/10.1109/DSAA60987.2023.10302600

Khan A, Mobaraki EB. Interpretability Methods for Graph Neural Networks. In Manolopoulos Y, Zhou ZH, editors, 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings). doi: 10.1109/DSAA60987.2023.10302600

Khan, Arijit ; Mobaraki, Ehsan B. / Interpretability Methods for Graph Neural Networks. 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. editor / Yannis Manolopoulos ; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings).

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title = "Interpretability Methods for Graph Neural Networks",

keywords = "explainable AI, graph neural networks, interpretability",

author = "Arijit Khan and Mobaraki, \{Ehsan B.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 ; Conference date: 09-10-2023 Through 12-10-2023",

year = "2023",

doi = "10.1109/DSAA60987.2023.10302600",

language = "English",

series = "2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - GEN

T1 - Interpretability Methods for Graph Neural Networks

AU - Khan, Arijit

AU - Mobaraki, Ehsan B.

PY - 2023

Y1 - 2023

KW - explainable AI

KW - graph neural networks

KW - interpretability

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U2 - 10.1109/DSAA60987.2023.10302600

DO - 10.1109/DSAA60987.2023.10302600

M3 - Conference contribution

AN - SCOPUS:85178997671

T3 - 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings

BT - 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings

A2 - Manolopoulos, Yannis

A2 - Zhou, Zhi-Hua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

Y2 - 9 October 2023 through 12 October 2023

ER -

Interpretability Methods for Graph Neural Networks

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this