单轨迹 map equation
Single-trajectory map equation
AI总结：
本文重新审视 map equation，提出其 single-trajectory map equation 原始形式，以揭示随机游走稳态极限下隐藏的机制，并获得更均衡、更不易过拟合的社区结构。
摘要：
社区检测是在以网络表示的复杂系统中识别模块结构的过程，已成为多个科学领域中的有效工具。map equation 是一种基于网络上随机游走的信息论框架，也是一种特别流行的社区检测方法。尽管它在许多应用中表现出色，但 map equation 的内在运作机制尚未得到充分研究。本文重新审视 map equation 的原始表述，并指出存在一种“原始形式”，我们将其称为 single-trajectory map equation（单轨迹 map equation）。这种原始形式揭示了 map equation 原理背后隐藏在随机游走稳态极限中的许多细节。最重要的是，single-trajectory map equation 能提供更均衡的社区结构，并自然降低 map equation 中过拟合现象的倾向。
英文摘要：
Community detection, the process of identifying module structures in complex systems represented on networks, is an effective tool in various fields of science. The map equation, which is an information-theoretic framework based on the random walk on a network, is a particularly popular community detection method. Despite its outstanding performance in many applications, the inner workings of the map equation have not been thoroughly studied. Herein, we revisit the original formulation of the map equation and address the existence of its ``raw form,'' which we refer to as the single-trajectory map equation. This raw form sheds light on many details behind the principle of the map equation that are hidden in the steady-state limit of the random walk. Most importantly, the single-trajectory map equation provides a more balanced community structure, naturally reducing the tendency of the overfitting phenomenon in the map equation.
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。
