Structural-Aware Key Node Identification in Hypergraphs via Representation Learning and Fine-Tuning
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918105603637248 |
|---|---|
| author | Ni, Xiaonan Mei, Guangyuan Zhang, Su-Su Chen, Yang Xu, Xin Liu, Chuang Zhan, Xiu-Xiu |
| author_facet | Ni, Xiaonan Mei, Guangyuan Zhang, Su-Su Chen, Yang Xu, Xin Liu, Chuang Zhan, Xiu-Xiu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20682 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structural-Aware Key Node Identification in Hypergraphs via Representation Learning and Fine-Tuning Ni, Xiaonan Mei, Guangyuan Zhang, Su-Su Chen, Yang Xu, Xin Liu, Chuang Zhan, Xiu-Xiu Social and Information Networks 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. |
| title | Structural-Aware Key Node Identification in Hypergraphs via Representation Learning and Fine-Tuning |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2507.20682 |