SOURCE-LINKED INTELLIGENCE
Repurposing Unified Topological Signatures for Graph Representation Learning
Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However, their discriminative power is upper-bounded by the Weisfeiler--Lehman (1-WL) graph isomorphism test. This prevents GNNs from distinguishing certain non-isomorphic graphs with identical local neighborhood structures, often leading to similar graph representations. Unified Topological Signatures (UTS) capture compact, multi-scale representation of global graph topology derived from persistent homology. We introduce two
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T12:07:05.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.