HopRank is a self-supervised LLM-tuning method that turns node classification into link prediction via hierarchical hop-based preference sampling, matching supervised GNN performance with zero labeled data on text-attributed graphs.
URL https://openreview.net/forum?id=L2jRavXRxs
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
LLMs achieve strong results on text-attributed graphs using only node textual descriptions, while most methods for encoding graph structure deliver marginal or negative gains.
citing papers explorer
-
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
HopRank is a self-supervised LLM-tuning method that turns node classification into link prediction via hierarchical hop-based preference sampling, matching supervised GNN performance with zero labeled data on text-attributed graphs.
-
When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected
LLMs achieve strong results on text-attributed graphs using only node textual descriptions, while most methods for encoding graph structure deliver marginal or negative gains.