Adversarial attacks on graph-level embedding methods: a case study
As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper expl...
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| Published in | Annals of mathematics and artificial intelligence Vol. 91; no. 2-3; pp. 259 - 285 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.06.2023
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1012-2443 1573-7470 |
| DOI | 10.1007/s10472-022-09811-4 |
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| Abstract | As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper explores the effects of such attacks on some graph datasets by applying different graph-level embedding techniques. The main attack strategy involves manipulating training data to produce an altered model. In this context, our goal is to go in-depth about methods, resources, experimental settings, and performance results to observe and study all the aspects that derive from the attack stage. |
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| AbstractList | As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper explores the effects of such attacks on some graph datasets by applying different graph-level embedding techniques. The main attack strategy involves manipulating training data to produce an altered model. In this context, our goal is to go indepth about methods, resources, experimental settings, and performance results to observe and study all the aspects that derive from the attack stage. Keywords Adversarial attacks * Adversarial machine learning * Graph embedding * Graph neural networks * Graph classification Mathematics subject classification (2010) 68T01 * 68T07 * 68R10 Mathematics subject classification (2020) 68T01 * 68T07 * 92B20 * 68R10 As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper explores the effects of such attacks on some graph datasets by applying different graph-level embedding techniques. The main attack strategy involves manipulating training data to produce an altered model. In this context, our goal is to go in-depth about methods, resources, experimental settings, and performance results to observe and study all the aspects that derive from the attack stage. |
| Audience | Academic |
| Author | Maddalena, Lucia Manzo, Mario Guarracino, Mario Rosario Giordano, Maurizio |
| Author_xml | – sequence: 1 givenname: Maurizio orcidid: 0000-0001-9917-7591 surname: Giordano fullname: Giordano, Maurizio email: maurizio.giordano@cnr.it organization: High Performance Computing and Networking Institute (ICAR), National Research Council (CNR) – sequence: 2 givenname: Lucia orcidid: 0000-0002-0567-4624 surname: Maddalena fullname: Maddalena, Lucia organization: High Performance Computing and Networking Institute (ICAR), National Research Council (CNR) – sequence: 3 givenname: Mario orcidid: 0000-0001-8727-9865 surname: Manzo fullname: Manzo, Mario organization: Information Technology Services, University of Naples “L’Orientale” – sequence: 4 givenname: Mario Rosario orcidid: 0000-0003-2870-8134 surname: Guarracino fullname: Guarracino, Mario Rosario organization: Department of Economics and Law, University of Cassino and Southern Lazio |
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| CitedBy_id | crossref_primary_10_3389_fmed_2023_1114362 crossref_primary_10_1145_3638057 crossref_primary_10_3390_math11081761 crossref_primary_10_3390_electronics12092132 |
| Cites_doi | 10.1145/3447556.3447566 10.1098/rsif.2012.0220 10.1007/s10472-019-09640-y 10.1109/TKDE.2018.2807452 10.1186/s12859-020-03564-9 10.1016/j.socnet.2007.11.001 10.1631/FITEE.2000352 10.1016/0005-2795(75)90109-9 10.1109/ACCESS.2018.2807385 10.1016/j.knosys.2018.03.022 10.1186/s13326-018-0189-6 10.1109/SPW.2018.00016 10.1126/science.1260419 10.1109/TCBB.2021.3078089 10.3390/app9050909 10.1109/ICDM.2005.132 10.1145/3450569.3463560 10.1007/978-3-030-00470-5_23 10.1007/978-3-030-92121-7_19 10.1109/BIBM.2018.8621500 10.1201/9781003202653 10.1086/228631 10.1145/2046684.2046692 10.1007/978-3-030-73241-7_8 10.1007/978-3-031-12515-7_1 10.1021/jm00106a046 10.1609/aaai.v29i1.9491 10.1109/TKDE.2022.3201243 10.1145/3340531.3411866 |
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| DOI | 10.1007/s10472-022-09811-4 |
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| SubjectTerms | Algorithms Analysis Artificial Intelligence Case studies Classification Complex Systems Computational linguistics Computer Science Cybersecurity Embedding Graph representations Language processing Machine learning Mathematics Methods Natural language interfaces Neural networks Training |
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| Title | Adversarial attacks on graph-level embedding methods: a case study |
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