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Universal Graph Backdoor Defense: A Feature-based Homophily Perspective

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arXiv:2605. 16815v1 Announce Type: new Abstract: Graph neural networks (GNNs) have achieved remarkable success in relational learning.

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Authors: Mengting Pan, Fan Li, Chen Chen, Xiaoyang Wang
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Universal Graph Backdoor Defense: A Feature-based Homophily Perspective overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: under, rate, model. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2605.16815

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