Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs

Research article (IEEE Transactions on Computational Social Systems, 2023) · cited 37× · AI/ML
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Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs

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Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs. Retrieved May 24, 2026, from https://4ort.xyz/entity/motif-backdoor-rethinking-the-backdoor-attack-on-graph-neural-networks-via-motifs
MLA “Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/motif-backdoor-rethinking-the-backdoor-attack-on-graph-neural-networks-via-motifs.
BibTeX @misc{4ortxyz_motif-backdoor-rethinking-the-backdoor-attack-on-graph-neural-networks-via-motifs_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs}}, year = {2026}, url = {https://4ort.xyz/entity/motif-backdoor-rethinking-the-backdoor-attack-on-graph-neural-networks-via-motifs}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs — https://4ort.xyz/entity/motif-backdoor-rethinking-the-backdoor-attack-on-graph-neural-networks-via-motifs (retrieved 2026-05-24)

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