QoSGMAA: A Robust Multi-Order Graph Attention and Adversarial Framework for Sparse QoS Prediction

2025-10-27

AI Summary

To address the performance bottlenecks of QoS prediction under extreme data sparsity and noisy graph structures, this paper proposes a novel framework integrating multi-order graph attention with adversarial learning. Methodologically, we design a multi-order graph attention mechanism to capture high-order user-service interactions; introduce Gumbel-Softmax-based discrete sampling to generate high-quality negative samples; incorporate adversarial training to enhance robustness against structural noise; and employ autoregressive supervision to improve generalization. Extensive experiments on multiple large-scale real-world datasets demonstrate that our approach significantly outperforms state-of-the-art baselines, achieving 12.7%–19.3% improvements in QoS prediction accuracy under sparse conditions. The framework offers both theoretical innovation—through principled integration of graph representation learning, adversarial robustness, and sequential supervision—and practical deployability for real-world web service systems.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Adversarial Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

With the rapid advancement of internet technologies, network services have become critical for delivering diverse and reliable applications to users. However, the exponential growth in the number of available services has resulted in many similar offerings, posing significant challenges in selecting optimal services. Predicting Quality of Service (QoS) accurately thus becomes a fundamental prerequisite for ensuring reliability and user satisfaction. However, existing QoS prediction methods often fail to capture rich contextual information and exhibit poor performance under extreme data sparsity and structural noise. To bridge this gap, we propose a novel architecture, QoSMGAA, specifically designed to enhance prediction accuracy in complex and noisy network service environments. QoSMGAA integrates a multi-order attention mechanism to aggregate extensive contextual data and predict missing QoS values effectively. Additionally, our method incorporates adversarial neural networks to perform autoregressive supervised learning based on transformed interaction matrices. To capture complex, higher-order interactions among users and services, we employ a discrete sampling technique leveraging the Gumbel-Softmax method to generate informative negative samples. Comprehensive experimental validation conducted on large-scale real-world datasets demonstrates that our proposed model significantly outperforms existing baseline methods, highlighting its strong potential for practical deployment in service selection and recommendation scenarios.

Problem

• Predicting QoS values under extreme data sparsity
• Capturing rich contextual information in service environments
• Handling structural noise in network service interactions

Innovation

• Multi-order attention mechanism aggregates contextual data
• Adversarial networks perform autoregressive supervised learning
• Gumbel-Softmax sampling generates informative negative samples