Rethinking Sampling Strategy in Link Prediction

2026-06-19T08:52:18Z8ef34efd1662b7060e21389bd60d1ceb0488861f938fa92183e17f190345bc32
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

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Record · Rethinking Sampling Strategy in Link Prediction · Baitaphish