Attention-based graph neural networks: a survey
2026-05-12T08:52:13Z•4ba95ce959571b5f3103915eca520de3702dbb979e81c648881cae4d5f8e7e4b
LLMadversarial-aiattentionattributionbenchmarkbioinformaticscausal-inferencecreator-economydigital-twinfront-doorgraph-generationgraph-neural-networksgraph-transformermodel-safetyneuron-editingnode-insertionrecommender-systemsself-debiasingsurveysynthetic-networksvisualization
What happened
This RSS bundle contains multiple new arXiv publications (May 12, 2026) across graph ML, causal attribution in recommender systems, AI-human opinion dynamics, model safety, visualization, and trust in digital twin systems. Highlights: an up-to-date survey of attention-based GNNs (including graph transformers); ALM-MTA, a front-door causal multi-touch attribution approach applied at massive scale for creator-economy optimization; Astro Generative Network for controlled node insertion into observed graphs; GraphInstruct, a progressive benchmark diagnosing LLM graph-generation failures; MiRA, aブラ
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 4ba95ce959571b5f3103915eca520de3702dbb979e81c648881cae4d5f8e7e4b
- Enrichment time
- 2026-05-12T08:52:13Z
- AI-assisted enrichment
- Yes
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