# Dynamic hierarchies in temporal directed networks

## TL;DR

This paper extends the concept of hierarchy detection in directed networks to temporal settings, proposing methods to control rank fluctuations over time and demonstrating their computational properties and practical effectiveness.

## Contribution

It introduces two strategies for dynamic hierarchy detection in temporal networks, including a polynomial-time exact solution for one variant and an iterative approach for the NP-hard case.

## Key findings

- Polynomial-time solution for rank fluctuation penalization
- Iterative method for rank change point detection
- Empirical results show practical speed and sensible rankings

## Abstract

The outcome of interactions in many real-world systems can be often explained by a hierarchy between the participants. Discovering hierarchy from a given directed network can be formulated as follows: partition vertices into levels such that, ideally, there are only forward edges, that is, edges from upper levels to lower levels. In practice, the ideal case is impossible, so instead we minimize some penalty function on the backward edges. One practical option for such a penalty is agony, where the penalty depends on the severity of the violation. In this paper we extend the definition of agony to temporal networks. In this setup we are given a directed network with time stamped edges, and we allow the rank assignment to vary over time. We propose 2 strategies for controlling the variation of individual ranks. In our first variant, we penalize the fluctuation of the rankings over time by adding a penalty directly to the optimization function. In our second variant we allow the rank change at most once. We show that the first variant can be solved exactly in polynomial time while the second variant is NP-hard, and in fact inapproximable. However, we develop an iterative method, where we first fix the change point and optimize the ranks, and then fix the ranks and optimize the change points, and reiterate until convergence. We show empirically that the algorithms are reasonably fast in practice, and that the obtained rankings are sensible.