GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction

• URL: http://arxiv.org/abs/2408.10555v2
• Date: Thu, 12 Sep 2024 05:52:05 GMT
• Title: GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction
• Authors: Shengxiang Hu, Guobing Zou, Bofeng Zhang, Shaogang Wu, Shiyi Lin, Yanglan Gan, Yixin Chen,
• Abstract summary: We propose a novel Graph Collaborative Learning (GACL) framework for temporal prediction. It builds on a dynamic user-service graph to comprehensively model historical interactions. Experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal prediction.
• Score: 5.040979636805073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specific user-service invocations, which are critical for precise feature extraction within each time slice. Moreover, the prevalent use of RNNs for modeling temporal feature evolution patterns is constrained by their inherent difficulty in managing long-range dependencies, thereby limiting the detection of long-term QoS trends across multiple time slices. These shortcomings dramatically degrade the performance of temporal QoS prediction. To address the two issues, we propose a novel Graph Attention Collaborative Learning (GACL) framework for temporal QoS prediction. Building on a dynamic user-service invocation graph to comprehensively model historical interactions, it designs a target-prompt graph attention network to extract deep latent features of users and services at each time slice, considering implicit target-neighboring collaborative relationships and historical QoS values. Additionally, a multi-layer Transformer encoder is introduced to uncover temporal feature evolution patterns, enhancing temporal QoS prediction. Extensive experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal QoS prediction across multiple evaluation metrics, achieving the improvements of up to 38.80%.

Related papers

• Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network [0.47248250311484113]
  Quality-of-Service (QoS) prediction is a critical task in the service lifecycle. Traditional methods often encounter data sparsity and cold-start issues. We introduce a real-time, trust-aware framework for temporal prediction.
  arXiv  Detail & Related papers  (2024-10-23T11:01:39Z)
• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• A Novel Spatiotemporal Coupling Graph Convolutional Network [0.18130068086063336]
  This paper presents a novel Graph Contemporalal Networks (GCNs)-based dynamic estimator namely Spatio Coupling GCN (SCG) model with the three-fold ideas as below. The results demonstrate that SCG realizes higher accuracy compared with the state-of-the-arts, illustrating it can learn powerful representations to users and cloud services.
  arXiv  Detail & Related papers  (2024-08-09T02:02:01Z)
• Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting [65.40983982856056]
  We introduce STOIC, that leverages correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides 16% more accurate and better-calibrated forecasts.
  arXiv  Detail & Related papers  (2024-07-02T20:14:32Z)
• Large Language Model Meets Graph Neural Network in Knowledge Distillation [7.686812700685084]
  We propose a temporal-aware framework for predicting Quality of Service (QoS) in service-oriented architectures. Our proposed TOGCL framework significantly outperforms state-of-the-art methods across multiple metrics, achieving improvements of up to 38.80%.
  arXiv  Detail & Related papers  (2024-02-08T18:33:21Z)
• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• ARRQP: Anomaly Resilient Real-time QoS Prediction Framework with Graph Convolution [0.16317061277456998]
  We introduce a real-time prediction framework (called ARRQP) with a specific emphasis on improving resilience to anomalies in the data. ARRQP integrates both contextual information and collaborative insights, enabling a comprehensive understanding of user-service interactions. Results on the benchmark WS-DREAM dataset demonstrate the framework's effectiveness in achieving accurate and timely predictions.
  arXiv  Detail & Related papers  (2023-09-22T04:37:51Z)
• TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features [0.5161531917413706]
  Temporal Prediction is essential to identify a suitable service over time. Recent methods hardly achieved desired accuracy due to various limitations. This paper proposes a scalable strategy for Temporal Prediction using Multi-source Collaborative-Features.
  arXiv  Detail & Related papers  (2023-03-30T06:49:53Z)
• Predicting Temporal Sets with Deep Neural Networks [50.53727580527024]
  We propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective is to learn element relationship by constructing set-level co-occurrence graph. We design an attention-based module to adaptively learn the temporal dependency of elements and sets.
  arXiv  Detail & Related papers  (2020-06-20T03:29:02Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.