156 citations · 277 across the 20 of their papers we have counts for
20 papers
A Survey of LLM Alignment: Instruction Understanding, Intention Reasoning, and Reliable Generation
Zongyu Chang, Feihong Lu, Ziqin Zhu +10
Large language models have demonstrated exceptional capabilities in understanding and generation. However, in real-world scenarios, users' natural language expressions are often in…
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…
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Haonan Yuan, Qingyun Sun, Xingcheng Fu +4
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribu…
Does Graph Distillation See Like Vision Dataset Counterpart?
Beining Yang, Kai Wang, Qingyun Sun +5
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condens…
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
Qian Li, Cheng Ji, Shu Guo +3
Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenge…
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Qian Li, Shu Guo, Cheng Ji +3
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task,…
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
Qian Li, Shu Guo, Yangyifei Luo +4
The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning
Cheng Ji, Jianxin Li, Hao Peng +4
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. Howeve…
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems
Qian Li, Jianxin Li, Lihong Wang +6
Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power…
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Qingyun Sun, Jianxin Li, Haonan Yuan +5
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topolog…
Curvature Graph Generative Adversarial Networks
Jianxin Li, Xingcheng Fu, Qingyun Sun +4
Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. However, for non-Euclidean graph data, the existing GAN-based graph represent…
Graph Structure Learning with Variational Information Bottleneck
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming th…
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Xingcheng Fu, Jianxin Li, Jia Wu +6
Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existingGNNs embed graph data into Euclidean space and thus are less effective to cap…
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Yu He, Yangqiu Song, Jianxin Li +3
Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attent…