---
title: "Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes (Extended Abstract)"
authors:
  - "Jaewan Chun"
  - "Seokbum Yoon"
  - "Minyoung Choe"
  - "Geon Lee"
  - "Kijung Shin"
doi: "10.24963/ijcai.2026/922"
url: "https://ijcai.org/proceedings/2026/922"
pdf: "https://ijcai.org/proceedings/2026/0922.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8254-8259"
track: "Sister Conferences Best Papers"
keywords:
  - "Data Mining: Networks"
  - "AI: Data Mining"
  - "Data Mining: Mining graphs"
---

# Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes (Extended Abstract)

Jaewan Chun, Seokbum Yoon, Minyoung Choe, Geon Lee, Kijung Shin

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8254-8259. DOI: [10.24963/ijcai.2026/922](https://doi.org/10.24963/ijcai.2026/922)

## Abstract

In many real-world scenarios, interactions happen in a group-wise manner with multiple entities, and therefore, hypergraphs are a suitable tool to accurately represent such interactions. Various generative models have been proposed to explore fundamental mechanisms underlying hyperedge formation. However, most existing models do not account for node attributes, which can play a significant role in hyperedge formation. As a result, they fail to capture the interactions between structure and node attributes. To address this issue, we propose NoAH, a stochastic hypergraph generative model for attributed hypergraphs. NoAH utilizes the core–fringe node hierarchy to model hyperedge formation as a series of node attachments and determines attachment probabilities based on node attributes. We further introduce NoAHFit, a parameter learning procedure for fitting NoAH to a given real-world hypergraph. Through experiments, we show that NoAH with NoAHFit more accurately reproduces the structure–attribute interplay than baseline hypergraph generative models. Our code, supplementary materials, and datasets are provided at https://github.com/jaewan01/NoAH.