6 papers
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 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…
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…
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…
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…