POLE: Polarized Embedding for Signed Networks
- URL: http://arxiv.org/abs/2110.09899v2
- Date: Wed, 20 Oct 2021 18:20:25 GMT
- Title: POLE: Polarized Embedding for Signed Networks
- Authors: Zexi Huang, Arlei Silva, Ambuj Singh
- Abstract summary: Recent advances in machine learning for signed networks hold the promise to guide small interventions with the goal of reducing polarization in social media.
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.
We propose POLE, a signed embedding method for polarized graphs that captures both topological and signed similarities jointly.
- Score: 2.6546685109604304
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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