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Yunhui Liu

19 papers hereh-index 462 citations21 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author14
  • middle author4

Across the 18 of 19 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • cs.AI2
  • cs.SI1
same name
  • Yunhui Liu — 26 papers, h 19
  • Yunhui Liu — 18 papers, h 46
  • Yunhui Liu — 11 papers, h 3
  • Yunhui Liu — 10 papers, h 6
  • Yunhui Liu — 6 papers, h 4
  • Yunhui Liu — 5 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedReliable Node Similarity Matrix Guided Contrastive Graph Clustering

14 citations · 19 across the 18 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.SI2025

Dynamic Graph Recommendation via Sparse Augmentation and Singular Adaptation

Zhen Tao, Yuehang Cao, Yang Fang +3

Dynamic recommendation, focusing on modeling user preference from historical interactions and providing recommendations on current time, plays a key role in many personalized servi…

cs.LG2025

Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection

Yunhui Liu, Jiashun Cheng, Yiqing Lin +7

Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1) label scarcity stemming from the high cost of a…

cs.LG2025

Norm Augmented Graph AutoEncoders for Link Prediction

Yunhui Liu, Huaisong Zhang, Xinyi Gao +3

Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable repres…

cs.LG2025

Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs via Node-wise Multi-View Ensemble Distillation

Yunhui Liu, Zhen Tao, Xiang Zhao +3

Multiplex graphs, with multiple edge types (graph views) among common nodes, provide richer structural semantics and better modeling capabilities. Multiplex Graph Neural Networks (…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.