用图神经网络找关键用户，让其持中立立场以降低社交网络极化。
Opinion de-polarization in social networks with GNNs
- 基于图神经网络识别可降低极化的关键用户
- 使K个用户持中立立场可显著减少双极化网络的极化程度
- 适合研究社会极化、网络干预的学者与平台管理者
如今，社交媒体是政治辩论和观点交流的重要场所。大量研究表明，社交媒体高度极化，常见现象是回音室结构：用户被组织成对立社群，仅与意见相似者连接，从而限制信息接触。本文研究如何通过两个回音室网络降低极化。观察发现，若部分用户对某议题采取温和立场，网络整体极化程度将下降。基于此，我们提出一种高效算法，用于识别一组最优的K名用户，使其采纳中立立场可最小化网络极化。该算法采用图神经网络，相比传统方法更适用于大规模图结构。
原文摘要 · Abstract (English)
Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests that social media are highly polarized. A phenomenon that is commonly observed is the echo chamber structure, where users are organized in polarized communities and form connections only with similar-minded individuals, limiting themselves to consume specific content. In this paper we explore a way to decrease the polarization of networks with two echo chambers. Particularly, we observe that if some users adopt a moderate opinion about a topic, the polarization of the network decreases. Based on this observation, we propose an efficient algorithm to identify a good set of K users, such that if they adopt a moderate stance around a topic, the polarization is minimized. Our algorithm employs a Graph Neural Network and thus it can handle large graphs more effectively than other approaches
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