An intensely debated topic is whether political polarization on social media is on the rise. We
can investigate this question only if we can quantify polarization, by taking into account how …

This paper is the first to use contrastive learning to improve the robustness of graph
representation learning for signed bipartite graphs, which are commonly found in social …

Purpose The spread of rumors on social media has caused increasing concerns about an
under-informed or even misinformed public when it comes to scientific issues. However …

The management literature has extensively studied viral marketing in the last decade;
however, there is a lack of research in understanding network structures and the role of …

Community detection is one of the most fundamental problems in social network analysis,
while most existing research focuses on unsigned graphs. In real applications, social …

Because of the increasingly negative impacts of the echo chamber effect, such as the
dissemination of fake news and political polarization occurring in social networking services …

Signed networks are such social networks having both positive and negative links. A lot of
theories and algorithms have been developed to model such networks (eg, balance theory) …

From the 2016 US 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 …

Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for
the important task of node clustering has not been fully exploited. In particular, most state-of …

Signed graphs model complex relations using both positive and negative edges. Signed
graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the …