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

2025-07-28

AI Summary

Existing hypergraph key node identification methods predominantly rely on conventional graph models, which struggle to capture high-order, multi-way interactions—leading to insufficient accuracy and generalizability in importance assessment. To address this, we propose the AHGA framework: (1) an autoencoder learns high-order structural representations of hypergraphs; (2) a pre-trained hypergraph neural network (HGNN) models complex higher-order dependencies; and (3) task-driven fine-tuning via active learning bridges the gap between synthetic data and real-world scenarios. AHGA is the first method to precisely identify multifunctional key nodes that simultaneously exhibit high influence and strong structural disruption capability. Evaluated on eight real-world hypergraph datasets, AHGA achieves an average 37.4% performance gain over classical centrality measures, significantly enhancing robustness and practicality in key node detection.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Identifying key nodes in hypergraphs with polyadic interactions
• Bridging synthetic-real data gap via active fine-tuning
• Enhancing node influence and structural disruption analysis

Innovation

• Autoencoder extracts higher-order structural features
• HyperGraph neural network pre-trains node representations
• Active learning fine-tunes for real-world data robustness