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Explainability Techniques for Graph Convolutional Networks
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (Computer Vision)ORCID iD: 0000-0001-5211-6388
2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Graph Networks are used to make decisions in potentially complex scenarios but it is usually not obvious how or why they made them. In this work, we study the explainability of Graph Network decisions using two main classes of techniques, gradient-based and decomposition-based, on a toy dataset and a chemistry task. Our study sets the ground for future development as well as application to real-world problems.

Place, publisher, year, edition, pages
2019.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-260507OAI: oai:DiVA.org:kth-260507DiVA, id: diva2:1355769
Conference
International Conference on Machine Learning (ICML) Workshops, 2019 Workshop on Learning and Reasoning with Graph-Structured Representations
Note

QC 20191001

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved

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fulltext(9606 kB)6 downloads
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File name FULLTEXT01.pdfFile size 9606 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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Baldassarre, FedericoAzizpour, Hossein
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf