9 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.AI2023
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
Meng Liu, Ke Liang, Yue Liu +3
Temporal graph clustering (TGC) is a crucial task in temporal graph learning. Its focus is on node clustering on temporal graphs, and it offers greater flexibility for large-scale…
cs.CV2023★ 2 cited
Pseudo-label Correction and Learning For Semi-Supervised Object Detection
Yulin He, Wei Chen, Ke Liang +3
Pseudo-Labeling has emerged as a simple yet effective technique for semi-supervised object detection (SSOD). However, the inevitable noise problem in pseudo-labels significantly de…
cs.LG2023★ 9 cited
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +7
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample m…