发表机构
The University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文首次形式化社交网络回声室检测的目标函数，提出基于傅里叶变换和半定松弛的可扩展算法，在合成与真实数据上优于现有方法。
AI 中文摘要
在本文中，我们研究了社交网络中回声室的检测问题，即识别一组在某个话题上意见一致、但与其余节点意见相左的节点。我们认为，该问题不同于其他社交网络分析问题（如社区检测），也不同于其他图问题（如最大图割和最大团）。据我们所知，我们是首个利用集合函数的傅里叶变换理论（Stobbe 和 Krause, 2012）来形式化回声室检测目标函数的研究。我们提出了可扩展的半定松弛方法，并通过内点法和稀疏线性代数进行求解。实验上，在小型合成实验中，我们的算法比竞争方法更好地恢复了真实回声室。在大型真实世界数据集上，我们的算法产生的回声室具有比竞争方法更好的网络属性。为了独立验证我们提出的目标函数，我们在一个小型真实世界数据集上表明，我们的算法找到的回声室与已停用用户的一致性比竞争方法更高。
英文摘要
In this paper, we study the detection of an echo chamber in a social network, i.e., the identification of a set of nodes that agree on a topic, while disagreeing with the rest of nodes. We argue that this problem is different from other social network analysis problems such as community detection, and from other graph problems such as maximum graph cut and maximum clique. To the best of our knowledge, we are the first to formalize the objective function of echo chamber detection, by using the theory of Fourier transforms of set functions (Stobbe and Krause, 2012). We propose scalable semidefinite relaxation, solved via an interior point method and sparse linear algebra. Experimentally, our algorithm recovers the ground truth echo chamber better than competing methods on small synthetic experiments. Our algorithm produces echo chambers with better network properties than competing methods on large real-world datasets. To independently validate our proposed objective function, we show that our algorithm finds echo chambers with more agreements with suspended users than competing methods on a small real-world dataset.
