DGHSA: derivative graph-based hypergraph structure attack
DGHSA: derivative graph-based hypergraph structure attack
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Publisher
London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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Contents
Hypergraph Neural Networks (HGNNs) have been significantly successful in higher-order tasks. However, recent study have shown that they are also vulnerable to adversarial attacks like Graph Neural Networks. Attackers fool HGNNs by modifying node links in hypergraphs. Existing adversarial attacks on HGNNs only consider feasibility in the targeted at...
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Full title
DGHSA: derivative graph-based hypergraph structure attack
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TN_cdi_doaj_primary_oai_doaj_org_article_7f2c73417f3146ecbd49fc4e96ace4f2
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7f2c73417f3146ecbd49fc4e96ace4f2
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-79824-y
