AI RESEARCH
Learning Time-Varying Graphs from Incomplete Graph Signals
arXiv CS.LG
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ArXi:2510.17903v2 Announce Type: replace-cross This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unified non-convex optimization framework to simultaneously recover a sequence of graph Laplacian matrices while reconstructing the unobserved signal entries.