论文标题
网络鹰队的过程模型，用于探索社会动物互动中的潜在层次结构
Network Hawkes Process Models for Exploring Latent Hierarchy in Social Animal Interactions
论文作者
论文摘要
基于群体的社会主导地位在动物行为研究中具有重要的兴趣。研究经常记录随着时间的推移观察到的积极相互作用，因此可以捕获这种动态层次结构的模型至关重要。传统排名方法总结了跨时间的交互，仅使用汇总计数。取而代之的是，我们利用交互时间戳，提出了一系列具有潜在等级的网络点过程模型。我们仔细设计了这些模型，以结合动物相互作用数据的重要特征，包括赢家效应，爆发和造成型现象。通过迭代构建和评估这些模型，我们到达了最终的马尔可夫调制霍克斯工艺（C-MMHP），该过程最好地表征了在交互数据中观察到的所有上述模式。我们使用模拟和真实数据比较所有模型。使用统计开发的诊断观点，我们证明C-MMHP模型优于其他方法，捕获相关的潜在排名结构，从而导致对真实数据的有意义的预测。
Group-based social dominance hierarchies are of essential interest in animal behavior research. Studies often record aggressive interactions observed over time, and models that can capture such dynamic hierarchy are therefore crucial. Traditional ranking methods summarize interactions across time, using only aggregate counts. Instead, we take advantage of the interaction timestamps, proposing a series of network point process models with latent ranks. We carefully design these models to incorporate important characteristics of animal interaction data, including the winner effect, bursting and pair-flip phenomena. Through iteratively constructing and evaluating these models we arrive at the final cohort Markov-Modulated Hawkes process (C-MMHP), which best characterizes all aforementioned patterns observed in interaction data. We compare all models using simulated and real data. Using statistically developed diagnostic perspectives, we demonstrate that the C-MMHP model outperforms other methods, capturing relevant latent ranking structures that lead to meaningful predictions for real data.
