6 papers · 1 filter
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
Junshu Sun, Wanxing Chang, Qingming Huang +1
Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream tasks. However, extending th…
Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
Junshu Sun, Wanxing Chang, Qingming Huang +1
Graph neural networks (GNNs) tightly couple their input-output parameters to dataset-specific feature spaces and target sets, exhibiting limited transferability across different da…
Edit Less, Achieve More: Dynamic Sparse Neuron Masking for Lifelong Knowledge Editing in LLMs
Jinzhe Liu, Junshu Sun, Shufan Shen +2
Lifelong knowledge editing enables continuous, precise updates to outdated knowledge in large language models (LLMs) without computationally expensive full retraining. However, exi…
Relieving the Over-Aggregating Effect in Graph Transformers
Junshu Sun, Wanxing Chang, Chenxue Yang +2
Graph attention has demonstrated superior performance in graph learning tasks. However, learning from global interactions can be challenging due to the large number of nodes. In th…
Towards Dynamic Message Passing on Graphs
Junshu Sun, Chenxue Yang, Xiangyang Ji +2
Message passing plays a vital role in graph neural networks (GNNs) for effective feature learning. However, the over-reliance on input topology diminishes the efficacy of message p…
Scalable Graph Compressed Convolutions
Junshu Sun, Shuhui Wang, Chenxue Yang +1
Designing effective graph neural networks (GNNs) with message passing has two fundamental challenges, i.e., determining optimal message-passing pathways and designing local aggrega…