Network node immunization: improving Netshield algorithm through random rooted forests
2026-06-04T08:52:17Z•47ed125fa1f130ec0309575d13af0f03ced18ec4c514640bfd5f4255bd985757
LLM-multiagentMastodonNetshieldconsensus-dynamicsdecentralized-moderationepidemic-modelingfederationgraph-algorithmshealthcare-analyticsmajority-illusionmalware-propagation-defensesmisinformation-detectionnetwork-immunizationpolitical-influencepublic-policy-simulationrandom-spanning-forestreinforcement-learningresearch-collectionsocial-networksspectral-methodsweakly-supervised-learning
What happened
Collection of new CS/AI/complex-systems arXiv papers (2026-06-04). Highlights: a graph-spectral algorithmic advance for multiple-node immunization (K-shield) improving Netshield via random-walk kernels and random spanning forests; formal/empirical studies of social-network phenomena (majority-illusion detection complexity, dynamics of consensus in multi-LLM-agent networks, forecasting political news engagement, and weakly-supervised detection of media criticism); analyses of decentralized moderation at scale (Mastodon governance); and applied ML in healthcare/teamwork dynamics and uncertainty‑
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 47ed125fa1f130ec0309575d13af0f03ced18ec4c514640bfd5f4255bd985757
- Enrichment time
- 2026-06-04T08:52:17Z
- AI-assisted enrichment
- Yes
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