POLE：符号网络的极化嵌入
POLE: Polarized Embedding for Signed Networks
摘要：
从2016年美国总统大选到2021年国会大厦骚乱，再到与COVID-19相关的错误信息的传播，许多人将当今社会的严重分裂归咎于社交媒体。针对签名网络的机器学习的最新进展有望引导小干预，以减少社交媒体中的两极分化。然而，现有的模型在预测用户之间的冲突（或负面链接）方面尤其无效。这是由于链接符号和网络结构之间的强相关性，其中极化社区之间的负链接太稀疏，即使是最先进的方法也无法预测。为了解决这个问题，我们首先设计了一个分区不可知的符号图的基础上的符号随机游走的极化措施，并表明，许多现实世界中的图是高度极化。然后，我们提出了POLE（POLarized Embedding for signed networks），这是一种用于极化图的符号嵌入方法，它通过符号自协方差联合捕获拓扑和符号相似性。通过大量的实验，我们表明，POLE显着优于国家的最先进的方法，在有符号的链接预测，特别是负链接的增益高达一个数量级。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
