GraphSSR introduces an adaptive SSR pipeline with SSR-SFT data synthesis and SSR-RL (Authenticity-Reinforced and Denoising-Reinforced stages) to overcome one-size-fits-all subgraph noise in zero-shot LLM graph reasoning.
Are large language models in-context graph learners?arXiv preprint arXiv:2502.13562, 2025
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NOPE coarsens graphs via neighborhood interference rather than selfish pairwise matching to reach linear memory and near-linear time, with NOPE* variant delivering 1.8-10x speedups and comparable or better learning results than full graphs or LLM reasoning.
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Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
GraphSSR introduces an adaptive SSR pipeline with SSR-SFT data synthesis and SSR-RL (Authenticity-Reinforced and Denoising-Reinforced stages) to overcome one-size-fits-all subgraph noise in zero-shot LLM graph reasoning.
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Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
NOPE coarsens graphs via neighborhood interference rather than selfish pairwise matching to reach linear memory and near-linear time, with NOPE* variant delivering 1.8-10x speedups and comparable or better learning results than full graphs or LLM reasoning.