18 citations · 37 across the 29 of their papers we have counts for
9 papers · 1 filter
Node Role-Guided LLMs for Dynamic Graph Clustering
Dongyuan Li, Ying Zhang, Yaozu Wu +1
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing how complex real-world systems evolve over time. However, existing methods are…
Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
Dongyuan Li, Shun Zheng, Chang Xu +2
Time series forecasting has attracted significant attention in the field of AI. Previous works have revealed that the Channel-Independent (CI) strategy improves forecasting perform…
ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework
Yusong Wang, Chuang Yang, Jiawei Wang +5
Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating…
Event-Aware Prompt Learning for Dynamic Graphs
Xingtong Yu, Ruijuan Liang, Renhe Jiang +4
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural net…
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
Shiyin Tan, Jaeeon Park, Dongyuan Li +2
In the field of multi-document summarization (MDS), transformer-based models have demonstrated remarkable success, yet they suffer an input length limitation. Current methods apply…
Revisiting Dynamic Graph Clustering via Matrix Factorization
Dongyuan Li, Satoshi Kosugi, Ying Zhang +3
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix facto…
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dongyuan Li, Shiyin Tan, Ying Zhang +4
Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the suc…
Community-Invariant Graph Contrastive Learning
Shiyin Tan, Dongyuan Li, Renhe Jiang +2
Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations. However, mainstream GCL m…
A Survey on Deep Active Learning: Recent Advances and New Frontiers
Dongyuan Li, Zhen Wang, Yankai Chen +3
Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label new selected samples in a human-in-the-loop m…