A Novel Spatiotemporal Coupling Graph Convolutional Network

TL;DR

This paper introduces a novel spatiotemporal graph convolutional network that effectively models dynamic user-service interactions for improved QoS estimation accuracy.

Contribution

It proposes a unified GCN-based model incorporating tensor products and tensor factorization to better capture spatial and temporal patterns in QoS data.

Findings

SCG outperforms existing methods in QoS estimation accuracy.

The model effectively captures complex spatiotemporal patterns.

Experiments on large-scale datasets validate its superior performance.

Abstract

Dynamic Quality-of-Service (QoS) data capturing temporal variations in user-service interactions, are essential source for service selection and user behavior understanding. Approaches based on Latent Feature Analysis (LFA) have shown to be beneficial for discovering effective temporal patterns in QoS data. However, existing methods cannot well model the spatiality and temporality implied in dynamic interactions in a unified form, causing abundant accuracy loss for missing QoS estimation. To address the problem, this paper presents a novel Graph Convolutional Networks (GCNs)-based dynamic QoS estimator namely Spatiotemporal Coupling GCN (SCG) model with the three-fold ideas as below. First, SCG builds its dynamic graph convolution rules by incorporating generalized tensor product framework, for unified modeling of spatial and temporal patterns. Second, SCG combines the heterogeneous GCN layer with tensor factorization, for effective representation learning on bipartite user-service graphs. Third, it further simplifies the dynamic GCN structure to lower the training difficulties. Extensive experiments have been conducted on two large-scale widely-adopted QoS datasets describing throughput and response time. The results demonstrate that SCG realizes higher QoS estimation accuracy compared with the state-of-the-arts, illustrating it can learn powerful representations to users and cloud services.