4 papers
Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
Xiang Wu, Rong-Hua Li, Xunkai Li +3
Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characteristics. Recent research shows t…
Towards Cost-effective LLMs Routing with Batch Prompting
Haotian Xu, Kangfei Zhao, Jiadong Xie
Large Language Model (LLM) serving systems must balance task performance against monetary cost. Two prominent optimization techniques have emerged independently: LLM routing, which…
Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix
Jinhao Zhang, Kangfei Zhao, Qiuhao Zeng +1
Transformer-based architectures have become the dominant paradigm for Continuous-Time Dynamic Graph (CTDG) learning, yet their performance remains limited on temporally shifted dat…
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning
Xiang Wu, Xunkai Li, Rong-Hua Li +2
Dynamic graphs (DGs), which capture time-evolving relationships between graph entities, have widespread real-world applications. To efficiently encode DGs for downstream tasks, mos…