  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.
