QoSDiff: An Implicit Topological Embedding Learning Framework Leveraging Denoising Diffusion and Adversarial Attention for Robust QoS Prediction

TL;DR

QoSDiff introduces a novel embedding learning framework that uses denoising diffusion and adversarial attention to improve QoS prediction accuracy, robustness, and generalization without explicit graph construction.

Contribution

It proposes a new implicit topological embedding learning method combining diffusion models and adversarial attention, overcoming limitations of GNN-based approaches.

Findings

Outperforms state-of-the-art baselines on large-scale datasets

Demonstrates superior robustness against noise and outliers

Shows strong cross-dataset generalization

Abstract

Accurate Quality of Service (QoS) prediction is fundamental to service computing, providing essential data-driven guidance for service selection and ensuring superior user experiences. However, prevalent approaches, particularly Graph Neural Networks (GNNs), heavily rely on constructing explicit user--service interaction graphs. Such reliance not only leads to the intractability of explicit graph construction in large-scale scenarios but also limits the modeling of implicit topological relationships and exacerbates susceptibility to environmental noise and outliers. To address these challenges, this paper introduces \emph{QoSDiff}, a novel embedding learning framework that bypasses the prerequisite of explicit graph construction. Specifically, it leverages a denoising diffusion probabilistic model to recover intrinsic latent structures from noisy initializations. To further capture high-order interactions, we propose an adversarial interaction module that integrates a bidirectional hybrid attention mechanism. This adversarial paradigm dynamically distinguishes informative patterns from noise, enabling a dual-perspective modeling of intricate user--service associations. Extensive experiments on two large-scale real-world datasets demonstrate that QoSDiff significantly outperforms state-of-the-art baselines. Notably, the results highlight the framework's superior cross-dataset generalization capability and exceptional robustness against observational noise.