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DOI: 10.4230/LIPIcs.TIME.2023.19

Time-Aware Robustness of Temporal Graph Neural Networks for Link Prediction (Extended Abstract)

Authors: Marco Sälzer and Silvia Beddar-Wiesing

Published in: LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)

Abstract

We present a first notion of a time-aware robustness property for Temporal Graph Neural Networks (TGNN), a recently popular framework for computing functions over continuous- or discrete-time graphs, motivated by recent work on time-aware attacks on TGNN used for link prediction tasks. Furthermore, we discuss promising verification approaches for the presented or similar safety properties and possible next steps in this direction of research.

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Marco Sälzer and Silvia Beddar-Wiesing. Time-Aware Robustness of Temporal Graph Neural Networks for Link Prediction (Extended Abstract). In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 19:1-19:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{salzer_et_al:LIPIcs.TIME.2023.19,
  author =	{S\"{a}lzer, Marco and Beddar-Wiesing, Silvia},
  title =	{{Time-Aware Robustness of Temporal Graph Neural Networks for Link Prediction}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{19:1--19:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.19},
  URN =		{urn:nbn:de:0030-drops-191094},
  doi =		{10.4230/LIPIcs.TIME.2023.19},
  annote =	{Keywords: graph neural networks, temporal, verification}
}

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Time-Aware Robustness of Temporal Graph Neural Networks for Link Prediction (Extended Abstract)

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