3 papers
cs.SI2025
Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
Yaowen Hu, Wenxuan Tu, Yue Liu +4
Deep graph clustering (DGC), which aims to unsupervisedly separate the nodes in an attribute graph into different clusters, has seen substantial potential in various industrial sce…
cs.LG2025
Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphs
Yaowen Hu, Wenxuan Tu, Yue Liu +5
Deep graph clustering (DGC) for attribute-missing graphs is an unsupervised task aimed at partitioning nodes with incomplete attributes into distinct clusters. Addressing this chal…
cs.LG2025
Dual Boost-Driven Graph-Level Clustering Network
John Smith, Wenxuan Tu, Junlong Wu +10
Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notabl…