Structural-Aware Key Node Identification in Hypergraphs via Representation Learning and Fine-Tuning

TL;DR

This paper introduces AHGA, a novel hypergraph neural network framework that effectively identifies key nodes by capturing higher-order interactions, outperforming classical methods and demonstrating robustness across diverse real-world hypergraphs.

Contribution

The paper presents a new hypergraph neural network framework, AHGA, combining autoencoding, pre-training, and active learning for superior key node identification.

Findings

AHGA outperforms classical baselines by approximately 37.4%.

Identified nodes have high influence and disruption capability.

The method generalizes well across diverse hypergraph topologies.

Abstract

Evaluating node importance is a critical aspect of analyzing complex systems, with broad applications in digital marketing, rumor suppression, and disease control. However, existing methods typically rely on conventional network structures and fail to capture the polyadic interactions intrinsic to many real-world systems. To address this limitation, we study key node identification in hypergraphs, where higher-order interactions are naturally modeled as hyperedges. We propose a novel framework, AHGA, which integrates an Autoencoder for extracting higher-order structural features, a HyperGraph neural network-based pre-training module (HGNN), and an Active learning-based fine-tuning process. This fine-tuning step plays a vital role in mitigating the gap between synthetic and real-world data, thereby enhancing the model's robustness and generalization across diverse hypergraph topologies. Extensive experiments on eight empirical hypergraphs show that AHGA outperforms classical centrality-based baselines by approximately 37.4%. Furthermore, the nodes identified by AHGA exhibit both high influence and strong structural disruption capability, demonstrating their superiority in detecting multifunctional nodes.