HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommendation systems. Current GNN-based approaches suffer from shallow text encoding and heavy dependence on labeled data, limiting their effectiveness in label-scarce settings. While large language models (LLMs) naturally address the text understanding gap with deep semantic reasoning, existing LLM-for-graph methods either still require abundant labels during training or fail to exploit the rich structural signals freely available in graph topology. Our key observation is that, in many real-world TAGs, edges predominantly connect similar nodes un...
| Authors | Ziqing Wang, Kaize Ding |
|---|---|
| Primary category | cs.CL |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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