HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization

• URL: http://arxiv.org/abs/2507.20490v1
• Date: Mon, 28 Jul 2025 02:58:21 GMT
• Title: HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization
• Authors: Yanheng Hou, Xunkai Li, Zhenjun Li, Bing Zhou, Ronghua Li, Guoren Wang,
• Abstract summary: We introduce HIAL (Hypergraph Active Learning), a native active learning framework designed specifically for hypergraphs.<n>We show that HIAL significantly outperforms state-of-the-art baselines in terms of performance, efficiency, generality, and robustness.
• Score: 25.226891661398444
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, Hypergraph Neural Networks (HNNs) have demonstrated immense potential in handling complex systems with high-order interactions. However, acquiring large-scale, high-quality labeled data for these models is costly, making Active Learning (AL) a critical technique. Existing Graph Active Learning (GAL) methods, when applied to hypergraphs, often rely on techniques like "clique expansion," which destroys the high-order structural information crucial to a hypergraph's success, thereby leading to suboptimal performance. To address this challenge, we introduce HIAL (Hypergraph Active Learning), a native active learning framework designed specifically for hypergraphs. We innovatively reformulate the Hypergraph Active Learning (HAL) problem as an Influence Maximization task. The core of HIAL is a dual-perspective influence function that, based on our novel "High-Order Interaction-Aware (HOI-Aware)" propagation mechanism, synergistically evaluates a node's feature-space coverage (via Magnitude of Influence, MoI) and its topological influence (via Expected Diffusion Value, EDV). We prove that this objective function is monotone and submodular, thus enabling the use of an efficient greedy algorithm with a formal (1-1/e) approximation guarantee. Extensive experiments on seven public datasets demonstrate that HIAL significantly outperforms state-of-the-art baselines in terms of performance, efficiency, generality, and robustness, establishing an efficient and powerful new paradigm for active learning on hypergraphs.

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