Recurrence in the evolution of air transport networks

Sugishita, Kashin; Masuda, Naoki

doi:10.1038/s41598-021-84337-z

“…By construction, the distance between two points on the trajectory reflects how different the networks at two times are. This property is potentially useful for downstream tasks such as identifying macroscopic states and dynamics of the system state of temporal networks [61,72], change-point analysis [17,36,55], anomaly detection [40], systematic identification of recurrence using recurrence quantification analysis [70,72], and link prediction [11,45].…”

Section: Discussionmentioning

confidence: 99%

Embedding and Trajectories of Temporal Networks

Chanon

¹

,

Livi

²

,

Masuda

³

2023

View full text Add to dashboard Cite

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling, which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days.

show abstract

“…Rather, we aim at capturing both structural and dynamical features of the evolving By construction, the distance between two points on the trajectory reflects how different the networks at two times are. This property is potentially useful for downstream tasks such as identifying macroscopic states and dynamics of the system state of temporal networks [61,70], change-point analysis [17,36,55], anomaly detection [40], systematic identification of recurrence using recurrence quantification analysis [68,70], and link prediction [11,45].…”

Section: Discussionmentioning

confidence: 99%

Embedding and trajectories of temporal networks

Chanon¹,

Livi²,

Masuda³

2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling, which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days.

show abstract

“…For instance, if a new flight route is created between two airports, passengers may take other flights to connect to other destinations, increasing the traffic on the corresponding routes [56]. Taking these interactions into account might open up new perspectives to study the evolution of these types of infrastructure networks [57].…”

Section: Discussionmentioning

confidence: 99%

From temporal network data to the dynamics of social relationships

Gelardi

¹

,

Bail

²

,

Barrat

³

et al. 2021

View full text Add to dashboard Cite

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. However, going from temporal network data (i.e. a stream of interactions between individuals) to a representation of the social group’s evolution remains a challenge. Indeed, the temporal network at any specific time contains only the interactions taking place at that time and aggregating on successive time-windows also has important limitations. Here, we present a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent: as the time we can invest in social relationships is limited, reinforcing a relationship with someone is done at the expense of our relationships with others. We implement this interdependence in a parsimonious two-parameter model and apply it to several human and non-human primates’ datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks. Our model solves a long-standing problem by providing a simple and natural way to describe the dynamic evolution of social networks, with far-reaching consequences for the study of social networks and social evolution.

show abstract

Recurrence in the evolution of air transport networks

Cited by 21 publications

References 43 publications

Embedding and Trajectories of Temporal Networks

Embedding and Trajectories of Temporal Networks

Embedding and trajectories of temporal networks

From temporal network data to the dynamics of social relationships

Contact Info

Product

Resources

About