455 citations · 2.1k across the 25 of their papers we have counts for
27 papers
HomoGCL: Rethinking Homophily in Graph Contrastive Learning
Wen-Zhi Li, Chang-Dong Wang, Hui Xiong +1
Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting" learning scheme. How…
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification
Wen-Zhi Li, Chang-Dong Wang, Hui Xiong +1
Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that of…
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering
Si-Guo Fang, Dong Huang, Chang-Dong Wang +1
The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post…
Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning
Si-Guo Fang, Dong Huang, Xiao-Sha Cai +3
Although previous graph-based multi-view clustering algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from…
Adaptively-weighted Integral Space for Fast Multiview Clustering
Man-Sheng Chen, Tuo Liu, Chang-Dong Wang +2
Multiview clustering has been extensively studied to take advantage of multi-source information to improve the clustering performance. In general, most of the existing works typica…
Strongly Augmented Contrastive Clustering
Xiaozhi Deng, Dong Huang, Ding-Hua Chen +2
Deep clustering has attracted increasing attention in recent years due to its capability of joint representation learning and clustering via deep neural networks. In its latest dev…
Vision Transformer for Contrastive Clustering
Hua-Bao Ling, Bowen Zhu, Dong Huang +3
Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation l…
Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks
Yuankun Xu, Dong Huang, Chang-Dong Wang +1
Deep clustering has shown its promising capability in joint representation learning and clustering via deep neural networks. Despite the significant progress, the existing deep clu…
Multi-party Secure Broad Learning System for Privacy Preserving
Xiao-Kai Cao, Chang-Dong Wang, Jian-Huang Lai +2
Multi-party learning is an indispensable technique for improving the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multi-part…
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks
Dong Huang, Ding-Hua Chen, Xiangji Chen +2
Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn t…
Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
Ling Huang, Can-Rong Guan, Zhen-Wei Huang +4
Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of captu…
Joint Multi-view Unsupervised Feature Selection and Graph Learning
Si-Guo Fang, Dong Huang, Chang-Dong Wang +1
Despite significant progress, previous multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure…
GSR: Global Graph Guided Session-based Recommendation
Zhi-Hong Deng, Chang-Dong Wang, Ling Huang +2
Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical b…
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity
Dong Huang, Chang-Dong Wang, Jian-Huang Lai
Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restrict…
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
Zi-Yuan Hu, Jin Huang, Zhi-Hong Deng +4
Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF method…
Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency
Youwei Liang, Dong Huang, Chang-Dong Wang +1
Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views. However, existing graph lear…
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection
Pei-Zhen Li, Ling Huang, Chang-Dong Wang +1
Network community detection is a hot research topic in network analysis. Although many methods have been proposed for community detection, most of them only take into consideration…
Ultra-Scalable Spectral Clustering and Ensemble Clustering
Dong Huang, Chang-Dong Wang, Jian-Sheng Wu +2
This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-…
DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System
Zhi-Hong Deng, Ling Huang, Chang-Dong Wang +2
In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almos…
Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities
Dong Huang, Chang-Dong Wang, Hongxing Peng +2
Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues…
Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond
Dong Huang, Chang-Dong Wang, Jian-Huang Lai +1
The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently…
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
Dong Huang, Chang-Dong Wang, Jian-Huang Lai +3
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that r…
Robust Ensemble Clustering Using Probability Trajectories
Dong Huang, Jian-Huang Lai, Chang-Dong Wang
Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly…
Community Detection Using Multilayer Edge Mixture Model
Han Zhang, Chang-Dong Wang, Jian-Huang Lai +1
A wide range of complex systems can be modeled as networks with corresponding constraints on the edges and nodes, which have been extensively studied in recent years. Nowadays, wit…