Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training

no code implementations • 18 Nov 2023 • Yuecheng Li, YanMing Hu, Lele Fu, Chuan Chen, Lei Yang, Zibin Zheng

However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance.

Community Detection Contrastive Learning +1

Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering

no code implementations • 22 Apr 2023 • Zixiao Yu, Lele Fu, Zhiling Cai, Zhoumin Lu

To well cope with the issues, we propose a hyper-Laplacian regularized concept factorization (HLRCF) in low-rank tensor space for multi-view clustering.

Clustering Multi-view Subspace Clustering

Multi-view Graph Convolutional Networks with Differentiable Node Selection

no code implementations • 9 Dec 2022 • Zhaoliang Chen, Lele Fu, Shunxin Xiao, Shiping Wang, Claudia Plant, Wenzhong Guo

Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN.

Graph Embedding Graph Learning +1

Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning

no code implementations • 16 Nov 2022 • Zhaoliang Chen, Lele Fu, Jie Yao, Wenzhong Guo, Claudia Plant, Shiping Wang

In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms.

MULTI-VIEW LEARNING