Graph Prediction in a Low-Rank and Autoregressive Setting

TL;DR

This paper introduces a convex optimization approach for predicting evolving graph data, emphasizing sparsity and low-rank structures, with empirical validation and insights into related open questions.

Contribution

It formulates a novel convex method for graph prediction that incorporates sparsity and low-rank constraints, providing theoretical guarantees and empirical comparisons.

Findings

The proposed algorithm effectively predicts evolving graphs.

Convex formulation yields oracle inequalities and efficient solvers.

Empirical results demonstrate competitive performance against existing methods.

Abstract

We study the problem of prediction for evolving graph data. We formulate the problem as the minimization of a convex objective encouraging sparsity and low-rank of the solution, that reflect natural graph properties. The convex formulation allows to obtain oracle inequalities and efficient solvers. We provide empirical results for our algorithm and comparison with competing methods, and point out two open questions related to compressed sensing and algebra of low-rank and sparse matrices.