SOURCE-LINKED INTELLIGENCE

Topology-induced Operators Reveal Complementary Graph Representations without Training

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph topology into vector representations, or embeddings. However, the extent to which embedding quality depends on model learning, rather than on the underlying topological transformations, remains unclear. Here, we show that informative embeddings can be derived without complicated model design and gradient-based training. Propagating random features through implicit hierarchical structures induced by random walks and anonymous walks yields embeddings that capture node proximity and

Read original source ↗ Open in workspace

recordType

paper

region

Global