8 citations · 10 across the 12 of their papers we have counts for
12 papers
Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks
Yurong Zhong, Zhe Xie, Weiling Li +1
An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e.g., a protein interacti…
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks
Yurong Zhong, Zhe Xie, Weiling Li +1
An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed…
Online Sparse Streaming Feature Selection Using Adapted Classification
RuiYang Xu, Di Wu, Xin Luo
Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly. E…
A Symmetry and Graph Regularized Nonnegative Matrix Factorization Model for Community Detection
Zhigang Liu, Xin Luo
Community is a fundamental and critical characteristic of a Large-scale Undirected Network (LUN) like a social network, making community detection a vital yet thorny issue in LUN r…
A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection
Zhigang Liu, Xin Luo
Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning. A…
Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction
Cheng Liang, Teng Huang, Yi He +3
User behavior data produced during interaction with massive items in the significant data era are generally heterogeneous and sparse, leaving the recommender system (RS) a large di…
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis
Ye Yuan, Xin Luo
A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numero…
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
Di Wu, Peng Zhang, Yi He +1
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications. Latent factor analysis (LFA) is a typical representation learning method…
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
Di Wu, Yi He, Xin Luo
A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system. To perform hi…
High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection
Zhigang Liu, Xin Luo
Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circl…
Adaptive Divergence-based Non-negative Latent Factor Analysis
Ye Yuan, Guangxiao Yuan, Renfang Wang +1
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative f…
Rising Novelties on Evolving Networks: Recent Behavior Dominant and Non-Dominant Model
Khushnood Abbas
Novelty attracts attention like popularity. Hence predicting novelty is as important as popularity. Novelty is the side effect of competition and aging in evolving systems. Recent…