GraphVec produces transferable fixed-dimensional graph embeddings via spectral features from multi-scale global graphs and a convergent mean-alignment procedure, outperforming baselines on cross-domain few-shot classification and clustering across 13 datasets.
Xingtong Yu, Chang Zhou, Zhongwei Kuai, Xinming Zhang, and Yuan Fang
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
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.
citing papers explorer
-
GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
GraphVec produces transferable fixed-dimensional graph embeddings via spectral features from multi-scale global graphs and a convergent mean-alignment procedure, outperforming baselines on cross-domain few-shot classification and clustering across 13 datasets.
-
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.