GNN prompting aims to adapt models across tasks and graphs without requiring extensive retraining. However, most existing graph prompt methods still require task-specific parameter updates and face the issue of generalizing across graphs, limiting their performance and undermining the core promise of prompting. In this...
Yaqi Chen, Shi-Xun Huang, Lei Wang et al.· Neural Networks· 0 citations
Modern data-intensive applications increasingly require database systems to manage structured records and graph data. This demand gives rise to graph-relational data management, spanning storage, query processing, and optimization across relational and graph data. In response, relational database extensions, multi-mode...
Ze-Peng Liu, Xin-Xin Huang, Xuan-Ming Liu et al.· 0 citations
CacheBridge is introduced, which co-designs architecture-indexed mapper support, attention-aligned calibration, and bounded mapper construction while retaining a closed-form affine interface for online deployment.
Xing-Yu Qu, Si-Yuan Lu, Z. Chen et al.· 4 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
