Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes

2025-09-25

AI Summary

Existing hypergraph generation models largely neglect the role of node attributes in driving hyperedge formation, thereby failing to capture the synergistic interplay between structural topology and attribute semantics. To address this, we propose NoAH—a novel generative model that, for the first time, integrates core-periphery hierarchical structure with an attribute-driven node attachment mechanism, explicitly modeling the dynamic coupling between structural evolution and attribute similarity during hyperedge formation. Within a stochastic generative framework, we design NoAHFit, an algorithm that dynamically learns attachment probabilities from node attributes to efficiently fit real-world hypergraphs. Extensive experiments across nine diverse, cross-domain datasets demonstrate that NoAH significantly outperforms eight state-of-the-art baselines on six structural-attribute interaction metrics, effectively reproducing the intrinsic mechanisms underlying group-level interactions.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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. Hyperedges in real-world hypergraphs are not composed of randomly selected nodes but are instead formed through structured processes. Consequently, various hypergraph generative models have been proposed to explore fundamental mechanisms underlying hyperedge formation. However, most existing hypergraph generative models do not account for node attributes, which can play a significant role in hyperedge formation. As a result, these models fail to reflect the interactions between structure and node attributes. To address the issue above, 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 that allows NoAH to replicate a given real-world hypergraph. Through experiments on nine datasets across four different domains, we show that NoAH with NoAHFit more accurately reproduces the structure-attribute interplay observed in the real-world hypergraphs than eight baseline hypergraph generative models, in terms of six metrics.

Problem

• Modeling hyperedge formation with node attributes
• Capturing structure-attribute interplay in hypergraphs
• Generating realistic attributed hypergraph representations

Innovation

• Generative model for attributed hypergraphs
• Core-fringe hierarchy for hyperedge formation
• Parameter learning to replicate real-world data