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NoAHFit: Attributed Hypergraph Parameter Learning

Updated 13 July 2026
  • NoAHFit is a procedure that fits a stochastic generative model for attributed hypergraphs using a hierarchical core–fringe structure and attribute-based affinities.
  • It partitions nodes into core and fringe via UMHS, then estimates seed distributions and affinity matrices to match observed hyperedge structures and attribute interplay.
  • Empirical evaluations show NoAHFit outperforms baselines by accurately reproducing structural marginals, hyperedge entropy, and node homophily across diverse datasets.

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