Graph Prediction in a Low-Rank and Autoregressive Setting

2012/05/07 by Emile Richard, Émile Richard, Pierre-Andre Savalle +5
Computer Science · Mathematics · #Advanced Graph Neural Networks #Error Correcting Code Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Recommender Systems and Techniques #stat.ML

paper · pdf · doi:10.48550/arxiv.1205.1406

openalex publication_date 2012/05/07 · arxiv created 2012/05/09 · arxiv updated 2012/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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