Fair automated assessment of noncompliance in cargo ship networks

Supervised temporal link prediction in large-scale real-world networks

Link prediction is a well-studied technique for inferring the missing edges between two nodes in some static representation of a network. In modern day social networks, the timestamps associated with each link can be used to predict future links between so-far unconnected nodes. In these so-called temporal networks, we speak of temporal link...

Understanding Dynamics of Truck Co-Driving Networks

The goal of this paper is to learn the dynamics of truck co-driving behaviour. Understanding this behaviour is important because co-driving has a potential positive impact on the environment. In the so-called co-driving network, trucks are nodes while links indicate that two trucks frequently drive together. To understand the network’s dynamics,...

Understanding behavioral patterns in truck co-driving networks

This paper examines the co-driving behavior of truck drivers using network analysis. From a unique spatiotemporal dataset encompassing more than 10 million measurements of trucks passing 17 different highway locations in the Netherlands, we extract a so-called co-driving network. In this network, nodes are truck drivers and edges represent pairs...

Imputation methods outperform missing-indicator for data missing completely at random