# Detecting sequences of system states in temporal networks

arXiv: https://arxiv.org/abs/1803.04755 · HTML: https://mlsanity.com/paper/1803.04755/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [An embedding-based distance for temporal graphs](https://mlsanity.com/paper/2401.12843/) (2024)
- [A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks](https://mlsanity.com/paper/2209.07332/) (2022)
- [FC-ADL: Efficient Microservice Anomaly Detection and Localisation Through Functional Connectivity](https://mlsanity.com/paper/2512.00844/) (2025)

## Abstract (arXiv)

Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system's states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein interactions, ecological interspecific interactions, functional connectivity in the brain and adaptive social networks.