Graph Prediction in a Low-Rank and Autoregressive Setting

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 lowrank 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.

Structural and functional discovery in dynamic networks with non-negative matrix factorization

“…This article is also in contrast to previous works that use temporal smoothness constraints for non-overlapping (hard) community detection [8], estimating time-varying network structure from covariate information [9], predicting network (link) structure [10], or anomaly detection [11,12].…”