研究记忆衰退如何影响网络在对抗攻击下的韧性，发现合理遗忘能提升稳定性。
Dynamic Homophily with Imperfect Recall: Modeling Resilience in Adversarial Networks
- 引入显式记忆衰减机制，结合余弦相似度建模网络演化
- 余弦相似度使稀疏网络稳定性提升30%
- 策略性遗忘可平衡网络鲁棒性与适应性，适合安全系统设计
本研究探讨同质性、记忆约束与对抗干扰共同作用下复杂网络的韧性与适应能力。我们构建新框架，将显式记忆衰减机制融入基于同质性的模型，并在多种图结构和对抗场景中系统评估其性能。通过在合成数据集上进行大规模实验，对比不同衰减函数、重连概率及相似度度量（主要为余弦相似度与传统指标如Jaccard相似度、基础边权），结果表明：在稀疏、凸形和模块化网络中，余弦相似度使稳定性指标最高提升30%。此外，优化的记忆价值度量显示，策略性遗忘可通过平衡网络鲁棒性与适应性增强整体韧性。研究强调，记忆与相似性参数必须与网络结构及对抗动态相匹配。通过量化记忆约束在同质性分析中的实际效益，本工作为社交系统、协作平台与网络安全等真实应用场景提供了可操作的优化思路。
原文摘要 · Abstract (English)
The purpose of this study is to investigate how homophily, memory constraints, and adversarial disruptions collectively shape the resilience and adaptability of complex networks. To achieve this, we develop a new framework that integrates explicit memory decay mechanisms into homophily-based models and systematically evaluate their performance across diverse graph structures and adversarial settings. Our methods involve extensive experimentation on synthetic datasets, where we vary decay functions, reconnection probabilities, and similarity measures, primarily comparing cosine similarity with traditional metrics such as Jaccard similarity and baseline edge weights. The results show that cosine similarity achieves up to a 30\% improvement in stability metrics in sparse, convex, and modular networks. Moreover, the refined value-of-recall metric demonstrates that strategic forgetting can bolster resilience by balancing network robustness and adaptability. The findings underscore the critical importance of aligning memory and similarity parameters with the structural and adversarial dynamics of the network. By quantifying the tangible benefits of incorporating memory constraints into homophily-based analyses, this study offers actionable insights for optimizing real-world applications, including social systems, collaborative platforms, and cybersecurity contexts.
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