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
6 Pith papers cite this work. Polarity classification is still indexing.
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GraspLLM extracts dataset-agnostic structural patterns via motif contrastive learning and aligns contextual subgraphs to LLM tokens, outperforming prior LLM-based methods on TAGs especially in zero-shot settings.
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
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.
GLIP is a joint GNN-LLM pretraining framework that uses augmentation, multi-token selection, a diffusion projector, and combined contrastive plus semantic losses to boost graph classification and reasoning after fine-tuning on limited labels.
G2LoRA proposes category-aware gradient projection and magnitude modulation within a unified graph-text alignment objective to mitigate interference and promote transfer in continual learning on text-attributed graphs.
citing papers explorer
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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.
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GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs
GraspLLM extracts dataset-agnostic structural patterns via motif contrastive learning and aligns contextual subgraphs to LLM tokens, outperforming prior LLM-based methods on TAGs especially in zero-shot settings.
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Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
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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.
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GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks
GLIP is a joint GNN-LLM pretraining framework that uses augmentation, multi-token selection, a diffusion projector, and combined contrastive plus semantic losses to boost graph classification and reasoning after fine-tuning on limited labels.
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G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
G2LoRA proposes category-aware gradient projection and magnitude modulation within a unified graph-text alignment objective to mitigate interference and promote transfer in continual learning on text-attributed graphs.