Network Hawkes process models for exploring latent hierarchy in social animal interactions

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Owen G. Ward, Jing Wu, Tian Zheng, Anna L. Smith, James P. Curley
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引用次数: 2

Abstract

Group-based social dominance hierarchies are of essential interest in understanding social structure (DeDeo & Hobson in, Proceedings of the National Academy of Sciences 118(21), 2021). Recent animal behaviour research studies can record aggressive interactions observed over time. Models that can explore the underlying hierarchy from the observed temporal dynamics in behaviours are therefore crucial. Traditional ranking methods aggregate interactions across time into win/loss counts, equalizing dynamic interactions with the underlying hierarchy. Although these models have gleaned important behavioural insights from such data, they are limited in addressing many important questions that remain unresolved. In this paper, we take advantage of the observed interactions' timestamps, proposing a series of network point process models with latent ranks. We carefully design these models to incorporate important theories on animal behaviour that account for dynamic patterns observed in the 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. As such, inference on our model components can be readily interpreted in terms of theories on animal behaviours. The probabilistic nature of our model allows us to estimate the uncertainty in our ranking. In particular, our model is able to provide insights into the distribution of power within the hierarchy which forms and the strength of the established hierarchy. 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.

探索动物社会互动中潜在等级的网络Hawkes过程模型
基于群体的社会支配等级对于理解社会结构至关重要(DeDeo &Hobson,《美国国家科学院院刊》118(21),2021)。最近的动物行为研究可以记录长期观察到的攻击性相互作用。因此,能够从观察到的行为时间动态中探索潜在层次的模型是至关重要的。传统的排名方法将不同时间的交互聚合成输赢计数,平衡了与底层层次的动态交互。尽管这些模型从这些数据中收集了重要的行为见解,但它们在解决许多尚未解决的重要问题方面是有限的。在本文中,我们利用观察到的相互作用的时间戳,提出了一系列具有潜在等级的网络点过程模型。我们精心设计了这些模型,以纳入动物行为的重要理论,这些理论解释了在相互作用数据中观察到的动态模式,包括赢家效应、破裂和成对翻转现象。通过迭代构建和评估这些模型,我们得到了最终的队列马尔可夫调制Hawkes过程(C-MMHP),它最好地表征了上述在相互作用数据中观察到的所有模式。因此,对我们模型成分的推断可以很容易地用动物行为理论来解释。我们模型的概率性质使我们能够估计排名中的不确定性。特别是,我们的模型能够提供对形成的层次结构中的权力分配和已建立的层次结构的强度的见解。我们使用模拟数据和真实数据对所有模型进行了比较。使用统计发展的诊断视角,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,从而对真实数据进行有意义的预测。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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