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Schema method generates large attributed graphs with scalability

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Schema, a novel method for generating large, attributed graphs with scalability in mind. Schema recursively decomposes a reference graph into a hierarchy of soft communities, enabling a three-stage generation process that synthesizes node attributes, generates intra-community edges, and models inter-community connections. This approach avoids forming the full adjacency matrix and operates on subgraphs, demonstrating superior structural fidelity and downstream utility compared to existing models on real-world attributed graphs, including those with up to 10 million nodes. AI

IMPACT Introduces a novel method for scalable graph generation, potentially improving AI model training on complex relational data.

RANK_REASON The cluster contains a research paper detailing a new method for graph generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Schema method generates large attributed graphs with scalability

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The cluster contains a research paper detailing a new method for graph generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ahmet T\"uzen, Helge Langseth, Kjetil N{\o}rv{\aa}g ·

    Scalable Hierarchical Graph Generation via Soft Community Structure

    arXiv:2610.12163v1 Announce Type: cross Abstract: Generating large attributed graphs requires reproducing the topology, generating attributes jointly with the structure, and remaining scalable. Many real-world graphs exist as a single large graph, so a generative model has to gen…

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