150 citations · 553 across the 38 of their papers we have counts for
6 papers · 1 filter
Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning
Ke Liang, Sihang Zhou, Yue Liu +3
Multimodal knowledge graphs (MKGs), which intuitively organize information in various modalities, can benefit multiple practical downstream tasks, such as recommendation systems, a…
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
Meng Liu, Ke Liang, Yue Liu +3
Temporal graph clustering (TGC) is a crucial task in temporal graph learning. Its focus is on node clustering on temporal graphs, and it offers greater flexibility for large-scale…
Message Intercommunication for Inductive Relation Reasoning
Ke Liang, Lingyuan Meng, Sihang Zhou +5
Inductive relation reasoning for knowledge graphs, aiming to infer missing links between brand-new entities, has drawn increasing attention. The models developed based on Graph Ind…
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
Wenxuan Tu, Bin Xiao, Xinwang Liu +3
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer f…
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…
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…