Performance of Higher-Order Networks in Reconstructing Sequential Paths: from Micro to Macro Scale

TL;DR

This paper evaluates how higher-order network models can accurately reconstruct various path characteristics, such as path lengths and motifs, across different datasets, enhancing understanding of sequential path data at multiple scales.

Contribution

It extends higher-order network analysis by identifying necessary model orders for reproducing path features and incorporating self-loops and start/end node considerations.

Findings

Higher-order models accurately reproduce path length distributions.

Sequential motif counts are well captured by models of appropriate order.

Analysis reveals where models overperform or underperform in different datasets.

Abstract

Activities such as the movement of passengers and goods, the transfer of physical or digital assets, web navigation and even successive passes in football, result in timestamped paths through a physical or virtual network. The need to analyse such paths has produced a new modelling paradigm in the form of higher-order networks which are able to capture temporal and topological characteristics of sequential data. This has been complemented by sequence mining approaches, a key example being sequential motifs measuring the prevalence of recurrent subsequences. Previous work on higher-order networks has focused on how to identify the optimal order for a path dataset, where the order can be thought of as the number of steps of memory encoded in the model. In this paper, we build on these approaches to consider which orders are necessary to reproduce different path characteristics, from path lengths to counts of sequential motifs, viewing paths generated from different higher-order models as null models which capture features of the data up to a certain order, and randomised otherwise. Furthermore, we provide an important extension to motif counting, whereby cases with self-loops, starting nodes, and ending nodes of paths are taken into consideration. Conducting a thorough analysis using path lengths and sequential motifs on a diverse range of path datasets, we show that our approach can shed light on precisely where models of different order overperform or underperform, and what this may imply about the original path data.