Snippet-Driven Supply Chain Discovery with LLMs: Scaling Visibility in China
2026-05-28T08:52:18Z•4a83145d309644d4790f5a3b38df7876d549774aae2e4d0057dd87b1c727815c
ChinaLLMShapley-valuesarxivcolored-configuration-modelcontent-moderationinfluence-attributioninformation-extractionknowledge-graphnull-modelprivacy-lawprovenancesocial-networkssupply-chainweb-scraping
What happened
Collection of recent arXiv CS / Social Intelligence papers (2026-05-28) covering methods and benchmarks with potential policy/privacy/moderation implications: (1) "Snippet-Driven Supply Chain Discovery with LLMs" — proposes a scalable supply-chain knowledge graph (SCKG) built from search snippets and LLM extraction for 130k+ Chinese firms with provenance metadata; (2) "Efficient Shapley-Based Influence Attribution in Social Networks" — ex-ante influence attribution framework using Shapley values, polynomial-time algorithms for single-step activation, hardness results and approximations; (3) "采
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 4a83145d309644d4790f5a3b38df7876d549774aae2e4d0057dd87b1c727815c
- Enrichment time
- 2026-05-28T08:52:18Z
- AI-assisted enrichment
- Yes
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