AI RESEARCH
GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
arXiv CS.LG
•
ArXi:2602.04244v2 Announce Type: replace Learning universal graph representations across heterogeneous domains is difficult because graph datasets differ in topology, node-attribute semantics, feature dimensions, and even attribute availability. We propose GraphVec, a language-model-free graph vectorization model that maps diverse graphs into transferable fixed-dimensional embeddings for graph-level tasks.