Signed Graph Learning: Algorithms and Theory

TL;DR

This paper introduces a novel method for learning signed graphs from data signals, employing the net Laplacian and ADMM optimization, with theoretical guarantees and applications to biological networks.

Contribution

It develops a new signed graph learning algorithm using the net Laplacian, with convergence proofs and a fast implementation, extending unsigned graph learning to signed graphs.

Findings

The method accurately recovers signed graphs in simulations.

It outperforms existing signed graph learning methods.

The approach is effective in gene regulatory network inference.

Abstract

Real-world data is often represented through the relationships between data samples, forming a graph structure. In many applications, it is necessary to learn this graph structure from the observed data. Current graph learning research has primarily focused on unsigned graphs, which consist only of positive edges. However, many biological and social systems are better described by signed graphs that account for both positive and negative interactions, capturing similarity and dissimilarity between samples. In this paper, we develop a method for learning signed graphs from a set of smooth signed graph signals. Specifically, we employ the net Laplacian as a graph shift operator (GSO) to define smooth signed graph signals as the outputs of a low-pass signed graph filter defined by the net Laplacian. The signed graph is then learned by formulating a non-convex optimization problem where the total variation of the observed signals is minimized with respect to the net Laplacian. The proposed problem is solved using alternating direction method of multipliers (ADMM) and a fast algorithm reducing the per-ADMM iteration complexity from quadratic to linear in the number of nodes is introduced. Furthermore, theoretical proofs of convergence for the algorithm and a bound on the estimation error of the learned net Laplacian as a function of sample size, number of nodes, and graph topology are provided. Finally, the proposed method is evaluated on simulated data and gene regulatory network inference problem and compared to existing signed graph learning methods.