Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019

Daniel Zügner; Amir Akbarnejad; Stephan Günnemann

doi:10.18420/inf2019_29

Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019

Daniel Zügner, Amir Akbarnejad, Stephan Günnemann

Informatics 26 - Chair of Professorship of Data Analytics and Machine Learning

Technical University of Munich

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

1 Scopus citations

Original language	English
Title of host publication	INFORMATIK 2019
Subtitle of host publication	50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft Konferenzbeitrage der 49. Jahrestagung der Gesellschaft fur Informatik
Editors	Klaus David, Kurt Geihs, Martin Lange, Gerd Stumme
Publisher	Gesellschaft fur Informatik (GI)
Pages	251-252
Number of pages	2
ISBN (Electronic)	9783885796886
DOIs	https://doi.org/10.18420/inf2019_29
State	Published - 2019
Event	49. Jahrestagung der Gesellschaft fur Informatik: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft, INFORMATIK 2019 - 49th Annual Meeting of the German Informatics Society: 50 years of the German Informatics Society - Computer Science for Society, INFORMATICS 2019 - Kassel, Germany Duration: 23 Sep 2019 → 26 Sep 2019

Publication series

Name	Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
Volume	294
ISSN (Print)	1617-5468
ISSN (Electronic)	2944-7682

Conference

Conference	49. Jahrestagung der Gesellschaft fur Informatik: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft, INFORMATIK 2019 - 49th Annual Meeting of the German Informatics Society: 50 years of the German Informatics Society - Computer Science for Society, INFORMATICS 2019
Country/Territory	Germany
City	Kassel
Period	23/09/19 → 26/09/19

Keywords

Adversarial machine learning
Deep learning
Graph neural networks

Access to Document

10.18420/inf2019_29

Cite this

Zügner, D., Akbarnejad, A., & Günnemann, S. (2019). Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019. In K. David, K. Geihs, M. Lange, & G. Stumme (Eds.), INFORMATIK 2019: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft Konferenzbeitrage der 49. Jahrestagung der Gesellschaft fur Informatik (pp. 251-252). (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Vol. 294). Gesellschaft fur Informatik (GI). https://doi.org/10.18420/inf2019_29

Zügner, Daniel ; Akbarnejad, Amir ; Günnemann, Stephan. / Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019. INFORMATIK 2019: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft Konferenzbeitrage der 49. Jahrestagung der Gesellschaft fur Informatik. editor / Klaus David ; Kurt Geihs ; Martin Lange ; Gerd Stumme. Gesellschaft fur Informatik (GI), 2019. pp. 251-252 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)).

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title = "Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019",

keywords = "Adversarial machine learning, Deep learning, Graph neural networks",

author = "Daniel Z{\"u}gner and Amir Akbarnejad and Stephan G{\"u}nnemann",

year = "2019",

doi = "10.18420/inf2019_29",

language = "English",

series = "Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)",

publisher = "Gesellschaft fur Informatik (GI)",

pages = "251--252",

editor = "Klaus David and Kurt Geihs and Martin Lange and Gerd Stumme",

booktitle = "INFORMATIK 2019",

note = "49. Jahrestagung der Gesellschaft fur Informatik: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft, INFORMATIK 2019 - 49th Annual Meeting of the German Informatics Society: 50 years of the German Informatics Society - Computer Science for Society, INFORMATICS 2019 ; Conference date: 23-09-2019 Through 26-09-2019",

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Zügner, D, Akbarnejad, A & Günnemann, S 2019, Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019. in K David, K Geihs, M Lange & G Stumme (eds), INFORMATIK 2019: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft Konferenzbeitrage der 49. Jahrestagung der Gesellschaft fur Informatik. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), vol. 294, Gesellschaft fur Informatik (GI), pp. 251-252, 49. Jahrestagung der Gesellschaft fur Informatik: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft, INFORMATIK 2019 - 49th Annual Meeting of the German Informatics Society: 50 years of the German Informatics Society - Computer Science for Society, INFORMATICS 2019, Kassel, Germany, 23/09/19. https://doi.org/10.18420/inf2019_29

Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019. / Zügner, Daniel; Akbarnejad, Amir; Günnemann, Stephan.
INFORMATIK 2019: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft Konferenzbeitrage der 49. Jahrestagung der Gesellschaft fur Informatik. ed. / Klaus David; Kurt Geihs; Martin Lange; Gerd Stumme. Gesellschaft fur Informatik (GI), 2019. p. 251-252 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Vol. 294).

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

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AU - Günnemann, Stephan

PY - 2019

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KW - Deep learning

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M3 - Conference contribution

AN - SCOPUS:85090862282

T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)

SP - 251

EP - 252

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A2 - Lange, Martin

A2 - Stumme, Gerd

PB - Gesellschaft fur Informatik (GI)

T2 - 49. Jahrestagung der Gesellschaft fur Informatik: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft, INFORMATIK 2019 - 49th Annual Meeting of the German Informatics Society: 50 years of the German Informatics Society - Computer Science for Society, INFORMATICS 2019

Y2 - 23 September 2019 through 26 September 2019

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Zügner D, Akbarnejad A, Günnemann S. Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019. In David K, Geihs K, Lange M, Stumme G, editors, INFORMATIK 2019: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft Konferenzbeitrage der 49. Jahrestagung der Gesellschaft fur Informatik. Gesellschaft fur Informatik (GI). 2019. p. 251-252. (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)). doi: 10.18420/inf2019_29

Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019

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Conference

Keywords

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