标题： 机器学习用于进化图论 显示英文标题

标题： Machine Learning for Evolutionary Graph Theory

摘要： 社区的稳定性——无论是生物、社会、经济、技术还是生态的，都取决于合作与欺骗之间的平衡。 虽然合作增强了社区，但自私的个体，或“欺骗者”，在不做出贡献的情况下利用集体利益。 如果欺骗者过于普遍，它们可能会引发合作和社区的崩溃，通常是以突然的方式。 一个关键挑战是确定这种崩溃的风险是否可以提前检测到。 为了解决这个问题，我们结合进化图论和机器学习来研究如何预测复杂网络上合作的瓦解。 通过向结构化种群中引入少数欺骗者，我们使用机器学习来检测和预测欺骗者的扩散以及合作的崩溃。 利用时间和结构数据，结果显示，预测准确性随着选择强度的增强和观察窗口的扩大而提高，其中CNN-Seq-LSTM和Seq-LSTM表现最好的模型。 此外，预测的准确性关键取决于合作者和欺骗者之间进行的游戏类型（即当背叛更有利时，准确性会提高），以及社区结构。 总体而言，这项工作将一种机器学习方法引入到进化图论中 abrupt shifts 的检测，并为预测和防止复杂社会网络中的合作崩溃提供了潜在策略。 显示英文摘要

摘要： The stability of communities - whether biological, social, economic, technological or ecological depends on the balance between cooperation and cheating. While cooperation strengthens communities, selfish individuals, or "cheaters," exploit collective benefits without contributing. If cheaters become too prevalent, they can trigger the collapse of cooperation and of the community, often in an abrupt manner. A key challenge is determining whether the risk of such a collapse can be detected in advance. To address this, we use a combination of evolutionary graph theory and machine learning to examine how one can predict the unravel of cooperation on complex networks. By introducing few cheaters into a structured population, we employ machine learning to detect and anticipate the spreading of cheaters and cooperation collapse. Using temporal and structural data, the presented results show that prediction accuracy improves with stronger selection strength and larger observation windows, with CNN-Seq-LSTM and Seq-LSTM best performing models. Moreover, the accuracy for the predictions depends crucially on the type of game played between cooperators and cheaters (i.e., accuracy improves when it is more advantageous to defect) and on the community structure. Overall, this work introduces a machine learning approach into detecting abrupt shifts in evolutionary graph theory and offer potential strategies for anticipating and preventing cooperation collapse in complex social networks.