156 citations · 277 across the 20 of their papers we have counts for
6 papers · 1 filter
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
Jiawen Qin, Pengfeng Huang, Qingyun Sun +3
Graph is a prevalent data structure employed to represent the relationships between entities, frequently serving as a tool to depict and simulate numerous systems, such as molecule…
DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
Haonan Yuan, Qingyun Sun, Zhaonan Wang +5
Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy resul…
GC-Bench: An Open and Unified Benchmark for Graph Condensation
Qingyun Sun, Ziying Chen, Beining Yang +6
Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core c…
Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction
Qian Li, Cheng Ji, Shu Guo +5
Multi-modal relation extraction (MMRE) is a challenging task that aims to identify relations between entities in text leveraging image information. Existing methods are limited by…
Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion
Qian Li, Shu Guo, Yinjia Chen +3
Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty…
Dynamic Graph Information Bottleneck
Haonan Yuan, Qingyun Sun, Xingcheng Fu +2
Dynamic Graphs widely exist in the real world, which carry complicated spatial and temporal feature patterns, challenging their representation learning. Dynamic Graph Neural Networ…