GraphReAct enables step-by-step graph inference by combining topological and semantic retrieval actions with context refinement in an LLM reasoning-acting loop, outperforming prior methods on six benchmarks.
Inductive representation learning on large graphs
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M2D distillation augments input graphs with model-derived features and structure, letting simple student GNNs match teacher performance while exposing mechanisms such as attention and fairness directly in the data.
citing papers explorer
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GraphReAct: Reasoning and Acting for Multi-step Graph Inference
GraphReAct enables step-by-step graph inference by combining topological and semantic retrieval actions with context refinement in an LLM reasoning-acting loop, outperforming prior methods on six benchmarks.
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From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning
M2D distillation augments input graphs with model-derived features and structure, letting simple student GNNs match teacher performance while exposing mechanisms such as attention and fairness directly in the data.