150 citations · 413 across the 20 of their papers we have counts for
11 papers · 1 filter
Reinforcement Graph Clustering with Unknown Cluster Number
Yue Liu, Ke Liang, Jun Xia +5
Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the pe…
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…
Dink-Net: Neural Clustering on Large Graphs
Yue Liu, Ke Liang, Jun Xia +4
Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the exis…
Deep Temporal Graph Clustering
Meng Liu, Yue Liu, Ke Liang +4
Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevert…
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
Lingyuan Meng, Ke Liang, Bin Xiao +5
Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. Th…
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…
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
Jingcan Duan, Siwei Wang, Pei Zhang +5
Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture ano…
GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
Xihong Yang, Erxue Min, Ke Liang +6
Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to group nodes into different clusters. The quality of contrastive samples is crucial for achie…
Mixed Graph Contrastive Network for Semi-Supervised Node Classification
Xihong Yang, Yiqi Wang, Yue Liu +5
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together…
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…