600 citations · 785 across the 10 of their papers we have counts for
10 papers
CLEAR: Cluster-based Prompt Learning on Heterogeneous Graphs
Feiyang Wang, Zhongbao Zhang, Junda Ye +2
Prompt learning has attracted increasing attention in the graph domain as a means to bridge the gap between pretext and downstream tasks. Existing studies on heterogeneous graph pr…
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces
Li Sun, Junda Ye, Jiawei Zhang +4
The sequential interaction network usually find itself in a variety of applications, e.g., recommender system. Herein, inferring future interaction is of fundamental importance, an…
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
Li Sun, Zhenhao Huang, Zixi Wang +3
Graphs are typical non-Euclidean data of complex structures. In recent years, Riemannian graph representation learning has emerged as an exciting alternative to Euclidean ones. How…
SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds
Junda Ye, Zhongbao Zhang, Li Sun +3
Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual infl…
Contrastive Graph Clustering in Curvature Spaces
Li Sun, Feiyang Wang, Junda Ye +2
Graph clustering is a longstanding research topic, and has achieved remarkable success with the deep learning methods in recent years. Nevertheless, we observe that several importa…
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
Li Sun, Junda Ye, Hao Peng +2
Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior…