150 citations · 422 across the 23 of their papers we have counts for
13 papers · 1 filter
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…
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…
RARE: Robust Masked Graph Autoencoder
Wenxuan Tu, Qing Liao, Sihang Zhou +5
Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts perf…
GANN: Graph Alignment Neural Network for Semi-Supervised Learning
Linxuan Song, Wenxuan Tu, Sihang Zhou +2
Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labe…
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Meng Liu, Ke Liang, Yawei Zhao +5
Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast…
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…
Cluster-guided Contrastive Graph Clustering Network
Xihong Yang, Yue Liu, Sihang Zhou +6
Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recentl…
Simple Contrastive Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +1
Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming g…
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
Siwei Wang, Xinwang Liu, Suyuan Liu +4
Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied…
Deep Graph Clustering via Dual Correlation Reduction
Yue Liu, Wenxuan Tu, Sihang Zhou +4
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, w…
Siamese Attribute-missing Graph Auto-encoder
Wenxuan Tu, Sihang Zhou, Yue Liu +1
Graph representation learning (GRL) on attribute-missing graphs, which is a common yet challenging problem, has recently attracted considerable attention. We observe that existing…
Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement
Chen Zhang, Siwei Wang, Wenxuan Tu +4
Multi-view clustering is an important yet challenging task in machine learning and data mining community. One popular strategy for multi-view clustering is matrix factorization whi…
Deep Fusion Clustering Network
Wenxuan Tu, Sihang Zhou, Xinwang Liu +4
Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structu…