Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark
2026-06-12T08:52:12Z•5207f9ae8943aeb1f99e4fd0afdaa33e66d164f088f0771a2fa7888ef85463fd
CBAMCCOMDigital Product Passport (DPP)Global Business ServicesInternational Data Spaces (IDSA)LLM multi-agentMIDSimOllivier-Ricci curvatureRegTechSORBagentic AIbenchmarkingcoarseningcold-start prediction','highlight salience','reading identitycommunity detectiongraph reductioninfluence maximisationinformation diffusionmultilayer networksrecommender systemssocial media simulationsparsificationsustainable intelligencetechnology roadmappingtwin transition
What happened
This feed contains seven recent research preprints across network science, AI-driven simulation, socio-technical business transformation, and computational text analysis. Key contributions: (1) SORB — a Spreading-Oriented Reduction Benchmark evaluating how graph reduction (sparsification/coarsening) affects influence maximisation across single- and multilayer networks, showing reduction effects depend on network type and downstream metric; (2) a Technology Roadmapping synthesis for Global Business Services (GBS) framing the "Twin Transition" (green + digital) and identifying resilient hubs and
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 5207f9ae8943aeb1f99e4fd0afdaa33e66d164f088f0771a2fa7888ef85463fd
- Enrichment time
- 2026-06-12T08:52:12Z
- AI-assisted enrichment
- Yes
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