PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking
Node ranking is a fundamental problem in graph information retrieval, measuring the relative importance of nodes and supporting a wide range of applications such as influence analysis, recommendation, and graph-based retrieval augmented generation. However, exact computation of graph-based ranking measures is often computationally prohibitive at scale. Existing GNN-based ranking methods provide scalable approximations, but they are typically tailored to individual ranking criteria and require retraining for each downstream task, which limits their transferability and efficiency. Recent graph pre-training approaches aim to enable knowledge transfer across tasks, yet their learning objectiv...
| Authors | Lujie Ban, Jiasheng shi, Yingli Zhou et al. |
|---|---|
| Primary category | cs.IR |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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