AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs Article Swipe

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Mengran Li , Chenwen Ding , Junzhou Chen , Wenqun Xing , Cong Ye , Ronghui Zhang , Songlin Zhuang , Jia Hu , Tony Z. Qiu , Huijun Gao ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2501.00743

Missing attribute issues are prevalent in the graph learning, leading to biased outcomes in Graph Neural Networks (GNNs). Existing methods that rely on feature propagation are prone to cold start problem, particularly when dealing with attribute resetting and low-degree nodes, which hinder effective propagation and convergence. To address these challenges, we propose AttriReBoost (ARB), a novel method that incorporates propagation-based method to mitigate cold start problems in attribute-missing graphs. ARB enhances global feature propagation by redefining initial boundary conditions and strategically integrating virtual edges, thereby improving node connectivity and ensuring more stable and efficient convergence. This method facilitates gradient-free attribute reconstruction with lower computational overhead. The proposed method is theoretically grounded, with its convergence rigorously established. Extensive experiments on several real-world benchmark datasets demonstrate the effectiveness of ARB, achieving an average accuracy improvement of 5.11% over state-of-the-art methods. Additionally, ARB exhibits remarkable computational efficiency, processing a large-scale graph with 2.49 million nodes in just 16 seconds on a single GPU. Our code is available at https://github.com/limengran98/ARB.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2501.00743
PDF: https://arxiv.org/pdf/2501.00743
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Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2501.00743

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Title: AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-01-01

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Authors: Mengran Li, Chenwen Ding, Junzhou Chen, Wenqun Xing, Cong Ye, Ronghui Zhang, Songlin Zhuang, Jia Hu, Tony Z. Qiu, Huijun Gao

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Landing page: https://arxiv.org/abs/2501.00743

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Concepts: Mathematical optimization, Mathematics, Computer science

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Cited by: 1

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