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arXiv AI By Ahmet T\"uzen, Helge Langseth, Kjetil N{\o}rv{\aa}g

Scalable Hierarchical Graph Generation via Soft Community Structure

Read the original on arXiv AI →

The paper introduces Schema, a generative model that recursively decomposes a single large attributed graph into a hierarchy of soft communities, assigning each node a membership distribution. Generation proceeds in three independently trained stages: synthesizing node attributes conditioned on soft memberships, generating intra-community edges from local structural context, and modeling inter-community connections via bridge nodes. Schema avoids constructing the full adjacency matrix, operates on subgraphs bounded by community size, and demonstrates superior balance between local and long-range structure while preserving downstream accuracy and scalability to graphs with up to 10 million nodes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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