150 citations · 382 across the 30 of their papers we have counts for
8 papers · 1 filter
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…
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
Yue Liu, Jun Xia, Sihang Zhou +7
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a fundamental yet challenging task. Benefiting from the powerful representation capabil…
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure
Ke Liang, Yue Liu, Sihang Zhou +5
Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications. Meanwhile, contrastive learning has been widely l…
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection
Rundong He, Rongxue Li, Zhongyi Han +1
Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually m…
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…
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…
Improved Dual Correlation Reduction Network
Yue Liu, Sihang Zhou, Xinwang Liu +2
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging…
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning
Xihong Yang, Xiaochang Hu, Sihang Zhou +2
Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regulariz…