17 citations · 27 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
Hierarchy-Consistent Learning and Adaptive Loss Balancing for Hierarchical Multi-Label Classification
Ruobing Jiang, Mengzhe Liu, Haobing Liu +1
Hierarchical Multi-Label Classification (HMC) faces critical challenges in maintaining structural consistency and balancing loss weighting in Multi-Task Learning (MTL). In order to…
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion
Xiang Li, Jianpeng Qi, Haobing Liu +6
Graph Neural Networks (GNNs) have demonstrated impressive performance across diverse graph-based tasks by leveraging message passing to capture complex node relationships. However,…
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…
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning
Ruobing Jiang, Yacong Li, Haobing Liu +1
Heterogeneous graphs (HGs) are composed of multiple types of nodes and edges, making it more effective in capturing the complex relational structures inherent in the real world. Ho…
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…