AI RESEARCH
Random Dot Product Graphs as Dynamical Systems: Limitations and Opportunities
arXiv CS.LG
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ArXi:2603.05703v1 Announce Type: cross Can we learn the differential equations governing the evolution of a temporal network? We investigate this within Random Dot Product Graphs (RDPGs), where each network snapshot is generated from latent positions evolving under unknown dynamics. We identify three fundamental obstructions: gauge freedom from rotational ambiguity in latent positions, realizability constraints from the manifold structure of the probability matrix, and trajectory recovery artifacts from spectral embedding.