ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
Graphtext: Graph rea- soning in text space.arXiv preprint arXiv:2310.01089
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
GTokenLLMs do not fully understand graph tokens, exhibiting over-sensitivity or insensitivity to instruction changes and relying heavily on text for reasoning even when graph information is preserved.
AgentGL is an RL-driven LLM agent framework for agentic graph learning that uses graph-native tools and curriculum training to outperform GraphLLM and GraphRAG baselines by up to 17.5% on node classification and 28.4% on link prediction across text-attributed graph benchmarks.
citing papers explorer
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Bridging Input Feature Spaces Towards Graph Foundation Models
ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
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Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
GTokenLLMs do not fully understand graph tokens, exhibiting over-sensitivity or insensitivity to instruction changes and relying heavily on text for reasoning even when graph information is preserved.
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AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
AgentGL is an RL-driven LLM agent framework for agentic graph learning that uses graph-native tools and curriculum training to outperform GraphLLM and GraphRAG baselines by up to 17.5% on node classification and 28.4% on link prediction across text-attributed graph benchmarks.