MoltGraph: A Longitudinal Temporal Graph Dataset of Moltbook for Coordinated-Agent Detection
2026-03-04T19:57:00Z•a32e26a87e50ad11bbd72541612b5bc2721b1eac325d063e37b5b5e968b3bd6d
agent-based-simulationcommunity-searchcoordinated-agentsdataset-releaseexposure-effectsfact-checkinggctamgraph-anomaly-detectiongraph-datasetshypergraph-centralityinfluence-operationsknowledge-graph-evaluationllm-integritymisinformationmoltgraphretrieval-augmented-generationsimulation-validitysocial-media-manipulationspatial-social-networksstructural-hallucinationtamvisibility-amplificationwkgfc
What happened
This arXiv feed contains multiple papers with direct relevance to information security, influence operations, and AI integrity. Key items: (1) MoltGraph — a new longitudinal graph-native dataset of an emerging social platform (Moltbook) that quantifies coordinated-agent behavior and shows rapid hub formation and large exposure amplification (coordinated posts have ~506% higher early interactions and ~243% higher downstream exposure), enabling reproducible study of coordinated manipulation and visibility effects; (2) Structural Hallucination in LLMs — defines “structural hallucination” and anet
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- a32e26a87e50ad11bbd72541612b5bc2721b1eac325d063e37b5b5e968b3bd6d
- Enrichment time
- 2026-03-04T19:57:00Z
- AI-assisted enrichment
- Yes
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