3 citations · 5 across the 3 of their papers we have counts for
5 papers
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…
Exploring the Tradeoff Between Diversity and Discrimination for Continuous Category Discovery
Ruobing Jiang, Yang Liu, Haobing Liu +2
Continuous category discovery (CCD) aims to automatically discover novel categories in continuously arriving unlabeled data. This is a challenging problem considering that there is…
Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs
Zhiyuan Zheng, Jianpeng Qi, Jiantao Li +3
Understanding the dynamic transition of motifs in temporal graphs is essential for revealing how graph structures evolve over time, identifying critical patterns, and predicting fu…
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…