14 citations · 32 across the 23 of their papers we have counts for
7 papers · 1 filter
Soft Graph Clustering for single-cell RNA Sequencing Data
Ping Xu, Pengfei Wang, Zhiyuan Ning +3
Clustering analysis is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis for elucidating cellular heterogeneity and diversity. Recent graph-based scRNA-seq cluste…
Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning
Hao Dong, Ziyue Qiao, Zhiyuan Ning +4
Temporal Knowledge Graphs (TKGs), as an extension of static Knowledge Graphs (KGs), incorporate the temporal feature to express the transience of knowledge by describing when facts…
Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking
Yihao Ai, Zhiyuan Ning, Weiwei Dai +5
Biomedical entity linking aims to map nonstandard entities to standard entities in a knowledge base. Traditional supervised methods perform well but require extensive annotated dat…
Rethinking Graph Contrastive Learning through Relative Similarity Preservation
Zhiyuan Ning, Pengfei Wang, Ziyue Qiao +2
Graph contrastive learning (GCL) has achieved remarkable success by following the computer vision paradigm of preserving absolute similarity between augmented views. However, this…
scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Ping Xu, Zhiyuan Ning, Pengjiang Li +5
Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially g…
Deep Cut-informed Graph Embedding and Clustering
Zhiyuan Ning, Zaitian Wang, Ran Zhang +8
Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However,…
Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +7
Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream ma…