5 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 3 cited
Weighted Graph Clustering via Scale Contraction and Graph Structure Learning
Haobing Liu, Yinuo Zhang, Tingting Wang +2
Graph clustering aims to partition nodes into distinct clusters based on their similarity, thereby revealing relationships among nodes. Nevertheless, most existing methods do not f…
cs.LG2025
Weighted Graph Structure Learning with Attention Denoising for Node Classification
Tingting Wang, Jiaxin Su, Haobing Liu +1
Node classification in graphs aims to predict the categories of unlabeled nodes by utilizing a small set of labeled nodes. However, weighted graphs often contain noisy edges and an…
cs.LG2024★ 5 cited
Incorporating Higher-order Structural Information for Graph Clustering
Qiankun Li, Haobing Liu, Ruobing Jiang +1
Clustering holds profound significance in data mining. In recent years, graph convolutional network (GCN) has emerged as a powerful tool for deep clustering, integrating both graph…