455 citations · 2.1k across the 25 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
Locally Weighted Ensemble Clustering
Dong Huang, Chang-Dong Wang, Jian-Huang Lai
Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attentio…