High-dimensional and incomplete (HDI) data are frequently encountered in diverse real-world applications involving complex interactions among numerous nodes. Approaches based on latent feature analysis (LFA) have proven effective in performing representation learning in HDI data. Nevertheless, they cannot handle the high-order connectivity among nodes in HDI data well, resulting in severe accuracy loss. To address the previously mentioned issue, we present a novel model in this paper, namely Graph Linear Convolution Pooling Network (GLCPN). The proposed GLCPN adopts the three-fold ideas. First, it leverages simplified graph convolutions to efficiently capture high-order connectivity among nodes for learning representations of matrix factorization. Second, a simple yet effective priori convolution operator is adopted by each graph neural layer to capture node-node collaboration for aggregation. Third, a locality-enhanced pooling scheme is designed to holistically utilize multi-layer representations of the neighborhood. Therefore, GLCPN can effectively acquire the hidden information in HDI data with high efficiency. In addition, we have conducted a theoretical analysis demonstrating that the proposed GLCPN is more expressive compared with existing graph neural networks for HDI data. Extensive experiments have been further conducted on ten well-established HDI datasets from various applications. The experimental results demonstrate that the proposed GLCPN significantly outperforms state-of-the-art models for learning representations in HDI data evaluated by accuracy and efficiency metrics.

In this article, we present the dynamic graph mixer (DGM), a novel model for learning spatiotemporal-individual coupled features from high-dimensional and incomplete (HDI) tensors, which frequently represent dynamic interactions among real-world data samples. In contrast to existing methods, the proposed DGM possesses the following three advantages when learning representations from HDI tensors. First, it performs light graph message passing based on the conjoint attentions learned by jointly modeling latent features and implicit structures to extract the high-order connectivity. Second, a multilayer nonlinear tensor neural network (TNN) is adopted to learn the intricate attribute features of node-node-time from different views. Third, it follows the Tucker decomposition paradigm in a data density-oriented modeling mechanism to integrate node representations, preserving the overall multidimensional interaction patterns. In addition, we provide theoretical evidence that the key components in DGM can significantly improve expressiveness. Extensive experiments conducted on eight testing datasets of HDI tensors demonstrate that DGM outperforms state-of-the-art methods in both learning accuracy and efficiency.

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 Network (GCN)-based dynamic QoS estimator namely Spatiotemporal Coupling GCN (SCG) model with the three-fold ideas as below. First, SCG builds its dynamic graph convolutional 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 time-varying 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.

Graph clustering is a fundamental and challenging learning task, which is conventionally approached by grouping similar vertices based on edge structure and feature similarity. In contrast to previous methods, in this paper, we investigate how multi-view feature propagation can influence cluster discovery in graph data. To this end, we present Discrete Multi-View Feature Propagation Preserving Graph Clustering (DMVFPPGC), a novel method that leverages multi-view feature propagation to enhance cluster identification in graph data. DMVFPPGC employs a unified objective function that utilizes graph topology and multi-view vertex features to determine vertex cluster membership, regularized by a module that supports key latent feature propagation. We derive an iterative algorithm to optimize this function, prove model convergence within a finite number of iterations, and analyze its computational complexity. Our experiments on various real-world graphs demonstrate the superior clustering performance of DMVFPPGC compared to well-established methods, manifesting its effectiveness across different scenarios.

High-Dimensional and Incomplete (HDI) data are frequently encountered in various Big Data-related applications. Despite its incompleteness, an HDI data repository contains rich knowledge and patterns concerning the complex interactions among numerous nodes. Recently, a Neural Network (NN)-based approach to Latent Feature Analysis (LFA) model becomes popular owing to its strong representation learning ability to HDI data. Nevertheless, existing NN-based LFA models neglect the inherent nonnegativity in most HDI data, resulting in representation accuracy loss. Motivated by this discovery, this study innovatively proposes a Fast Nonnegative AutoEncoder (FNAE)-based approach to LFA on HDI data, whose ideas are three-fold: a) constructing a multilayered autoencoder subject to nonnegativity constraints for high representation learning ability; b) incorporating the data density-oriented modeling mechanism into FNAE's input and output layers for high computational and storage efficiency; and c) implementing an Adam-based single latent factor-dependent, nonnegative and multiplicative update algorithm for efficient model training as well as fulfilling the nonnegativity constraints. Experimental results on eight commonly-adopted HDI matrices from industrial applications demonstrate that the proposed FNAE significantly outperforms several state-of-the-art NN-based LFA models in both estimation accuracy for missing links of an HDI matrix and computational efficiency.

Historical Quality-of-Service (QoS) data describing existing user-service invocations are vital to understanding user behaviors and cloud service conditions. Collaborative Filtering (CF) models based on Matrix Factorization (MF) have proven to be highly efficient in performing representation learning in QoS data. However, its performance is hindered by its linear inherence and implicit encoding of collaborative QoS signal. To address this critical issue, we present a novel approach in this article, dubbed asTwo-streamGraph convolutional network-incorporatedLatentFeatureAnalysis (TGLFA). The proposed TGLFA is significantly different from previous approaches to representation learning in QoS data in the following three aspects. First, it constructs a multilayer fully-connected network to capture the attribute characteristics representing the nonlinear latent features of service. Second, TGLFA constructs a biparty graph to represent the user-service interactions, where the light graph convolutional network is adopted to acquire the high-order connectivity in QoS data. Last, Aiming to improve computational efficiency, the proposed approach considers the mechanism for data density-oriented modeling when building the input and output layers. Detailed experimental results on eight large-scale cases constructed on two real QoS datasets demonstrate that the proposed TGLFA significantly outperforms its state-of-the-art peers in both estimation accuracy for missing QoS data and computational efficiency. The notable results show that TGLFA is a novel and effective approach to QoS data representation learning.

Historical Quality-of-Service (QoS) data regarding past user-service invocations are vital to understand the user behaviors and cloud service conditions. A Matrix Factorization (MF)-based Collaborative Filtering (CF) model has proven to be highly effective in performing representation learning to such QoS data. However, its performance is hindered by its linear interaction and implicit encoding of collaborative QoS signal. To address this critical issue, this paper presents a Two-stream Light Graph Convolution Network-based latent factor (TLGCN) model with the three-fold ideas: 1) constructing a multilayered and fully-connected network to represent services’ nonlinear latent features; 2) integrating the user-service interactions, i.e., the bipartite graph structure into the representation learning process with a light graph convolution network for illustrating the high-order connectivity information in QoS data; and 3) incorporating the data density-oriented modeling mechanism into the input and output of TLGCN for high computational efficiency. Experimental results on two real QoS datasets demonstrate that the proposed TLGCN model significantly outperforms its state-of-the-art peers in both estimation accuracy for missing QoS data and computational efficiency.