Rethinking Sampling Strategy in Link Prediction
2026-06-19T08:52:18Z•8ef34efd1662b7060e21389bd60d1ceb0488861f938fa92183e17f190345bc32
LLM-evasionMCMCadversarial-machine-learningbias-in-MLcontent-moderationdata-protectiondisinformationmobile-paymentsnetwork-analysisoperational-securityprivacypublic-health-dataredistrictingsocial-simulationweb-application
What happened
This arXiv feed contains multiple papers across network science, social simulation, and applied systems. Notable items with security implications: (1) "Simulation of Language Evolution under Regulated Social Media Platforms" (LLM + genetic algorithm) demonstrates realistic iterative content-evasion strategies that can bypass automated moderation—high misuse potential for policy evasion and illicit marketplaces; (2) "Toward Temporal Realism in City-Scale Crisis Response Simulation using LLM Agents" shows LLM-based social simulators lack realistic timing and recommends decoupling "when" (self-ex
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 8ef34efd1662b7060e21389bd60d1ceb0488861f938fa92183e17f190345bc32
- Enrichment time
- 2026-06-19T08:52:18Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.