150 citations · 745 across the 95 of their papers we have counts for
13 papers · 1 filter
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
Ke Liang, Lingyuan Meng, Meng Liu +6
Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research dir…
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…
Late Fusion Multi-view Clustering via Global and Local Alignment Maximization
Siwei Wang, Xinwang Liu, En Zhu
Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in v…
Multiple Kernel Clustering with Dual Noise Minimization
Junpu Zhang, Liang Li, Siwei Wang +4
Clustering is a representative unsupervised method widely applied in multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating comple…
Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph
Liang Li, Siwei Wang, Xinwang Liu +4
Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proved to be of…
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…
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…
DisARM: Displacement Aware Relation Module for 3D Detection
Yao Duan, Chenyang Zhu, Yuqing Lan +3
We introduce Displacement Aware Relation Module (DisARM), a novel neural network module for enhancing the performance of 3D object detection in point cloud scenes. The core idea of…
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…