Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory

Attention-deficit /hyperactivity disorder (ADHD) is a common neurodevelopmental syndrome characterized by age-inappropriate levels of motor activity, impulsivity and attention. The aim of the present study was to study diurnal variation of motor activity in adult ADHD patients, compared to healthy controls and clinical controls with mood and anxiety disorders. Wrist-worn actigraphs were used to record motor activity in a sample of 81 patients and 30 healthy controls. Time series from registrations in the morning and evening were analyzed using measures of variability, complexity and a newly developed method, the similarity algorithm, based on transforming time series into graphs. In healthy controls the evening registrations showed higher variability and lower complexity compared to morning registrations, however this was evident only in the female controls. In the two patient groups the same measures were not significantly different, with one exception, the graph measure bridges. This was the measure that most clearly separated morning and evening registrations and was significantly different both in healthy controls and in patients with a diagnosis of ADHD. These findings suggest that actigraph registrations, combined with mathematical methods based on graph theory, may be used to elucidate the mechanisms responsible for the diurnal regulation of motor activity.

Social Network Analysis

The study of social network analysis has grown in popularity in the past decades and has been used in many areas. It is an interesting and useful field that gained an increasing popularity due to the explosion of social media that has emerged with advances in communication systems, which play a critical role in forming human activities and interactions in social systems. The authors present some techniques from a data mining perspective and statistical graph measure that can be used in various applications such as to perform community detection, clustering in a social network, identify spurious and anomalous users, predict links between vertices in a social network, model and improve the information diffusion, design trust models, and improve other applications. Then, the authors provide a recent literature review of such applications and thus outline challenges of social network applications.

Robustness Measure of China’s Railway Network Topology Using Relative Entropy

This study focused on China’s railway network topology issue from a robustness measure perspective. Relative entropy is used in the study as a measurement of robustness of railway network topology. It is found that the entropy-based measure provides more informative analyses compared to a traditional graph measure. The results indicate that the railway network in the 12th five-year plan has improved robustness when compare to 2008 with respect to deliberate and random attacks.