A dynamic graph is approximated as a linear combination of latent adjacency matrices, estimated jointly from partial topology and smooth node signals, with better missing-edge reconstruction than tensor baselines.
A block coordinate descent method for regular- ized multiconvex optimization with applications to nonnegative tensor factorization and completion,
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Graph signal aware decomposition of dynamic networks via latent graphs
A dynamic graph is approximated as a linear combination of latent adjacency matrices, estimated jointly from partial topology and smooth node signals, with better missing-edge reconstruction than tensor baselines.