Graph Prediction in a Low-Rank and Autoregressive Setting

05/07/2012
by   Emile Richard, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/14/2012

Link Prediction in Graphs with Autoregressive Features

In the paper, we consider the problem of link prediction in time-evolvin...
research
06/05/2013

Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-rank Matrices

This paper considers compressed sensing and affine rank minimization in ...
research
11/13/2019

Graph-Induced Rank Structures and their Representations

A new framework is proposed to study rank-structured matrices arising fr...
research
05/03/2023

Multi-dimensional Signal Recovery using Low-rank Deconvolution

In this work we present Low-rank Deconvolution, a powerful framework for...
research
04/30/2012

Recovery of Low-Rank Plus Compressed Sparse Matrices with Application to Unveiling Traffic Anomalies

Given the superposition of a low-rank matrix plus the product of a known...
research
05/12/2023

On the Partial Convexification for Low-Rank Spectral Optimization: Rank Bounds and Algorithms

A Low-rank Spectral Optimization Problem (LSOP) minimizes a linear objec...
research
09/10/2018

Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise

In tracking of time-varying low-rank models of time-varying matrices, we...

Please sign up or login with your details

Forgot password? Click here to reset