AI 中文总结
本文提出RegRole技术，通过时序正则化NMF在时序动态网络中检测一致角色，生成角色转移矩阵，经多数据集实验验证其可降低预测误差、提升系统稳定性且适配大型稀疏图。
AI 中文摘要
本文提出了一种时序动态网络中的动态角色发现技术，利用时序正则化非负矩阵分解（Non-negative Matrix Factorization，NMF）。与现有动态角色分析技术不同，该技术在所有时间段内创建一组一致的角色，并生成一个通用转移矩阵来描述角色间的转移概率。我们还施加正则化惩罚以确保角色隶属度在时间段间不会发生剧烈变化，从而使模型对现实世界噪声更具鲁棒性。我们在5个真实世界数据集和1个人工模拟数据集上测试了数据，使用了工程化特征和自动生成的特征。结果表明，对于合适的正则化权重参数，所提出的正则化角色检测方法相比其他技术降低了预测误差。此外，对转移矩阵的轨迹分析显示，该方法能产生更稳定的系统，即个体更可能保持其角色，减少了任意转移。我们的模型学习时间对齐的角色，捕捉随时间的行为转移，并且能高效扩展到大型稀疏图。
英文摘要
This paper introduces a dynamic role discovery technique in temporal dynamic networks, utilizing temporally regularized Non-negative Matrix Factorization (NMF). Our technique differs from existing dynamic role analysis techniques by creating a consistent set of roles across all time periods, as well as a universal transition matrix that describes the probability of transitioning between roles. We also apply a regularization penalty to ensure that role membership does not change dramatically between time periods making our model more robust against real-world noise. We test our data on five real-world and one synthetically simulated dataset using both engineered and automatically generated features. We demonstrate that the proposed regularized role detection method, for appropriate regularization weight parameter reduces prediction errors compared to other techniques. Furthermore, trace analysis of the transition matrices indicates that our method yields a more stable system, that is, individuals are more likely to stay in their roles with fewer arbitrary transitions. Our model learns time-aligned roles, captures behavioral transitions over time, and scales efficiently to large and sparse graphs.
