POLE: Polarized Embedding for Signed Networks

TL;DR

This paper introduces POLE, a novel embedding method for signed networks that effectively predicts negative links and captures polarization, outperforming existing approaches especially in highly polarized real-world graphs.

Contribution

The paper proposes POLE, a new signed embedding technique that models polarization in signed networks using signed autocovariance, addressing the challenge of predicting sparse negative links.

Findings

POLE significantly outperforms state-of-the-art methods in signed link prediction.

POLE achieves up to tenfold improvements in predicting negative links.

Many real-world signed networks are highly polarized, as shown by the proposed measure.

Abstract

From the 2016 U.S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society. Recent advances in machine learning for signed networks hold the promise to guide small interventions with the goal of reducing polarization in social media. However, existing models are especially ineffective in predicting conflicts (or negative links) among users. This is due to a strong correlation between link signs and the network structure, where negative links between polarized communities are too sparse to be predicted even by state-of-the-art approaches. To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized graphs that captures both topological and signed similarities jointly via signed autocovariance. Through extensive experiments, we show that POLE significantly outperforms state-of-the-art methods in signed link prediction, particularly for negative links with gains of up to one order of magnitude.