39 citations · 108 across the 13 of their papers we have counts for
9 papers · 1 filter
Test-Time Training on Graphs with Large Language Models (LLMs)
Jiaxin Zhang, Yiqi Wang, Xihong Yang +6
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectivene…
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Jingcan Duan, Pei Zhang, Siwei Wang +5
Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to impro…
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Yi Wen, Suyuan Liu, Xinhang Wan +5
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information acr…
Scalable Incomplete Multi-View Clustering with Structure Alignment
Yi Wen, Siwei Wang, Ke Liang +6
The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or…
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
Xihong Yang, Cheng Tan, Yue Liu +7
Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning. The existing methods learn the sampling distri…
Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment
Jiaqi Jin, Siwei Wang, Zhibin Dong +2
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partia…