Patrick Colm Audley

Standard Graph Neural Networks are structurally constrained when mapping complex text attribution: linear aggregation in flat Euclidean space inevitably forces semantic drift. To map high-dimensional knowledge faithfully you have to transition to curved semantic manifolds, where the geometry itself carries the relational structure.

Across thirty years of building scientific analysis pipelines in genetics, satellite imagery and multi-continent high-resiliency financial applications, the common thread has been the same: representations must remain mathematically faithful to their underlying geometry, or they stop being interpretable the moment the data leaves your dev set.

I've recently open-sourced a framework that discovers emergent knowledge-graph relations in high-order semantic vector spaces through manifold learning and spectral analysis. Initial proofs, a small teaser, and the Python codebase live at paudley/nonlinear-semantic-graphs; the working paper that motivates the design is in Publications.

I'm looking to connect with researchers and applied scientists specialising in Topological Data Analysis, Geometric Deep Learning, and Knowledge Representation, especially anyone working on geodesic aggregation or spectral graph theory, to push these ideas into robust enterprise deployments. Comment on the LinkedIn original or drop me a line directly.