8 citations · 8 across the 3 of their papers we have counts for
6 papers
TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism
Hongjiang Chen, Pengfei Jiao, Ming Du +4
The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. However, TGNNs encounter fundame…
ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
Hongjiang Chen, Xin Zheng, Pengfei Jiao +5
Temporal graph neural networks (TGNNs) have gained significant traction for solving real-world temporal graph tasks. However, their interpretability remains limited, as most TGNNs…
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
Hongjiang Chen, Xin Zheng, Yixin Liu +7
Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the under…
Joint Optimization based on Two-phase GNN in RIS- and DF-assisted MISO Systems with Fine-grained Rate Demands
Huijun Tang, Jieling Zhang, Zhidong Zhao +3
Reconfigurable intelligent Surfaces (RIS) and half-duplex decoded and forwarded (DF) relays can collaborate to optimize wireless signal propagation in communication systems. Users…
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities
Pengfei Jiao, Hongjiang Chen, Xuan Guo +3
Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model compl…
Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs
Pengfe Jiao, Xinxun Zhang, Mengzhou Gao +2
Generative self-supervised learning (SSL), especially masked autoencoders (MAE), has greatly succeeded and garnered substantial research interest in graph machine learning. However…