Li Liang

Li Liang(李亮) is a Ph.D. student at National University of Defence Technology (NUDT), Hunan, China since 2021. He is supervised by Prof. Keqin Li and Prof. Xinwang Liu. Before that, he received the Master degree from NUDT in 2020, and got the Bachelor degree in 2018 from Huazhong University of Science and Technology (HUST), Wuhan, China. His research interests include machine learning, bioinformatics, and self-supervised learning. Any discussions or concerns are welcomed! Please contact me via e-mail:liliang1037@gmail.com

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News
  • [2024.07] One paper has been accepted by IEEE TNNLS.
  • [2024.05] One paper has been accepted by IEEE TNNLS.
  • [2024.03] I serve as a reviewer for IEEE TPAMI.
  • [2024.01] One paper has been accepted by IEEE TKDE.
  • [2023.09] I am visiting A*STAR for 18 months.
  • [2023.07] I was awarded CSC Scholarship.
  • [2023.04] One paper has been accepted by IEEE TNNLS.
  • [2023.04] One paper has been accepted by IEEE TKDE.
  • [2022.11] Two papers have been accepted by AAAI 2023.
  • [2022.10] I won the China National Scholarship (top 0.2%).
  • [2022.06] One paper has been accepted by ACMMM 2022.
  • [2022.06] One paper has been accepted by IEEE TNNLS.
  • [2021.12] I won the first prize of postgraduate academic scholarship at NUDT.
  • [2020.10] I won the China National Scholarship (top 0.2%).
  • [2020.10] I won the first prize of postgraduate academic scholarship at NUDT.
Research

Representative papers are highlighted.

Generalized ab Initio Refinement for Multi-view Bipartite Graph Clustering
Liang Li, Yuangang Pan, Jie Liu, Xinwang Liu, Kenli Li, Keqin Li
Preprint , 2024
Paper / Code

We propose a reinforced multi-view bipartite graph clustering.

Jet-BGC: Joint Latent Embedding and Structural Fusion Bipartite Graph Clustering
Liang Li, Jie Liu, Xinwang Liu, Kenli Li, Keqin Li
Preprint , 2023
[IEEE Xplore] / [pdf] / [Code]

We propose a novel Jet-BGC model that integrating embedding learning and Structural Fusion.

BGAE: Auto-encoding Multi-view Bipartite Graph Clustering
Liang Li, Yuangang Pan, Jie Liu, Yue Liu, Xinwang Liu, Kenli Li, Ivor W. Tsang, Keqin Li
IEEE TKDE (CCF-A, IF: 8.9), 2024
[IEEE Xplore] / [pdf] / [Code]

We rethink existing paradigms and find that a common design is to construct the bipartite graph directly from the input data, i.e. only consider the unidirectional "encoding" process. Inspired by the popular "encoding-decoding" design in deep learning, we transfer it into graph machine learning and propose a novel model.

Multi-view Bipartite Graph Clustering with Coupled Noisy Feature Filter
Liang Li, Junpu Zhang, Siwei Wang, Xinwang Liu, Kenli Li, Keqin Li
IEEE TKDE (CCF-A, IF: 8.9), 2023
[IEEE Xplore] / [pdf] / [Code]

One crucial finding is that the existence of noisy features will incur "anchor shift", which deviates from the potential centroids. We propose a novel noisy feature filter mechanism to remedy the anchor shift, and we theoretically analyze the bounds of the bipartite graph's sparsity.

Multiple Kernel Clustering with Dual Noise Minimization
Junpu Zhang, Liang Li (Co-first author), Siwei Wang, Jiyuan Liu, Yue Liu, Xinwang Liu, En Zhu,
ACM MM (CCF-A), 2022
[Link] / [pdf] / [Code]

We mathematically disassemble the noise within kernel partition into dual noise, namely, Null space noise (N-noise) and Column space noise (C-noise), and propose an elegant method to minimize them. We observe that dual noise will pollute the block diagonal structures. An interesting finding is that C-noise exhibits stronger destruction than N-noise.

Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph
Liang Li, Siwei Wang, Xinwang Liu, En Zhu, Li Shen, Kenli Li, Keqin Li
IEEE TNNLS (CCF-B, IF: 10.4), 2022
[IEEE Xplore] / [pdf] / [Code]

We investigate an important issue that how to localize the kernel matrix in multi-kernel clustering. Compared to the traditional KNN manner that neglects the ranking relationship of neighbors, this paper proposes a novel localized MKC algorithm coupled flexible graph learning, termd LSWMKC, which achieves fully exploring the latent local manifold.

TFMKC: Tuning-free Multiple Kernel Clustering Coupled with Diverse Partition Fusion
Junpu Zhang, Liang Li(Co-first author), Xinwang Liu
IEEE TNNLS (CCF-B, IF: 10.4) , 2024
[IEEE Xplore] / [pdf] / [Code]

We propose an elegant diverse kernel partition fusion framework to get the optimal partition.

Improved Dual Correlation Reduction Network
Yue Liu, Sihang Zhou, Xihong Yang, Xinwang Liu, Wenxuan Tu, Liang Li, Xin Xu, Fuchun Sun,
IEEE T-NNLS(CCF-B, IF: 10.4), 2024
[IEEE Xplore] / [pdf] / [Code] /

We explore deep-in reasons of representation collapse in deep graph clustering and improve the dual correlation reduction network with the affinity recovery strategy.

Simple Contrastive Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Siwei Wang, Ke Liang, Wenxuan Tu, Liang Li,
IEEE TNNLS (CCF-B, IF: 10.4), 2023
[IEEE Xplore] / [pdf] / [Code]

We propose to replace the complicated and consuming graph data augmentations by designing the parameter un-shared siamese encoders and perurbing the node embeddings.

Hard Sample Aware Network for Contrastive Deep Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Z. Wang, Ke Liang, Wenxuan Tu, Liang Li , Jingcan Duan, Cancan Chen
AAAI (CCF-A, Oral presentation), 2023
[Link] / [pdf] / [Code]

We propose Hard Sample Aware Network (HSAN) to mine both the hard positive samples and hard negative samples with a comprehensive similarity measure criterion and a general dynamic sample weighing strategy.

Let the data choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering
Pei Zhang, Siwei Wang, Liang Li, Changwang. Zhang, Xinwang Liu, En Zhu, Zhe Liu, Lu Zhou, Lei Luo,
AAAI (CCF-A), 2023
[Link] / [pdf] / [Code]

We propose to fuse diverse bipartite graphs across multiple views that can avoid tune the anchor number manually.

Service
  • Reviewer for TPAMI, TKDE, JAIR, TNNLS, TBD, ACM TOMM, Neurocomputing, DSP
  • Reviewer for NeurIPS24, ICLR25, AITATS25, ACM MM24/23, AAAI24/23, PRCV22
Award
  • CSC Scholarship, 2023.
  • National Scholarship, 2022.
  • First Prize of Postgraduate Academic Scholarship, 2021.
  • National Scholarship, 2020.
  • First Prize of Postgraduate Academic Scholarship, 2020.
  • Postgraduate Scientific Research Innovation Project of Hunan Province (CX20200008, CX20200084), 2020.
  • Second Prize of "HUAWEI" the 16th China Post-Graduate Mathematical Contest in Modeling, 2019.
  • Excellent Graduated Graduate Student, HUST, 2018.
  • Recommendation for admission to NUDT, 2018.

Design and source code from Jon Barron's website