Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach.

The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement.

Effect of on-demand vs continuous prescription of proton pump inhibitors on symptom burden and quality of life: results of a real-world randomized controlled trial in primary care patients with gastroesophageal reflux disease.

Objectives: This study aimed to assess the impact of on-demand versus continuous prescribing of proton pump inhibitors (PPIs) on symptom burden and health-related quality of life in patients with gastroesophageal reflux disease (GERD) presenting to primary care.

Methods: Thirty-six primary care centres across Europe enrolled adult GERD patients from electronic health records. Participants were randomised to on-demand or continuous PPI prescriptions and were followed for 8 weeks.

Influence of Information Blocking on the Spread of Virus in Multilayer Networks.

In this paper, we present the model of the interaction between the spread of disease and the spread of information about the disease in multilayer networks. Next, based on the characteristics of the SARS-CoV-2 virus pandemic, we evaluated the influence of information blocking on the virus spread. Our results show that blocking the spread of information affects the speed at which the epidemic peak appears in our society, and affects the number of infected individuals.

Sequential seeding in multilayer networks.

Multilayer networks are the underlying structures of multiple real-world systems where we have more than one type of interaction/relation between nodes: social, biological, computer, or communication, to name only a few. In many cases, they are helpful in modeling processes that happen on top of them, which leads to gaining more knowledge about these phenomena. One example of such a process is the spread of influence.

Analysis of group evolution prediction in complex networks.

In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict the evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic, and multistage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc.

Strategic distribution of seeds to support diffusion in complex networks.

Usually, the launch of the diffusion process is triggered by a few early adopters-i.e., seeds of diffusion.

Probing Limits of Information Spread with Sequential Seeding.

We consider here information spread which propagates with certain probability from nodes just activated to their not yet activated neighbors. Diffusion cascades can be triggered by activation of even a small set of nodes. Such activation is commonly performed in a single stage.

Quantifying layer similarity in multiplex networks: a systematic study.

Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.

A multilayer network dataset of interaction and influence spreading in a virtual world.

Presented data contains the record of five spreading campaigns that occurred in a virtual world platform. Users distributed avatars between each other during the campaigns. The processes varied in time and range and were either incentivized or not incentivized.

eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial.

Objective: The Learning Health System (LHS) requires integration of research into routine practice. 'eSource' or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a solution to the rising costs of research. We aimed to create and validate an eSource solution that would be readily extensible as part of a LHS.

Balancing Speed and Coverage by Sequential Seeding in Complex Networks.

Information spreading in complex networks is often modeled as diffusing information with certain probability from nodes that possess it to their neighbors that do not. Information cascades are triggered when the activation of a set of initial nodes - seeds - results in diffusion to large number of nodes. Here, several novel approaches for seed initiation that replace the commonly used activation of all seeds at once with a sequence of initiation stages are introduced.

TRANSFoRm eHealth solution for quality of life monitoring.

Patient Recorded Outcome Measures (PROMs) are an essential part of quality of life monitoring, clinical trials, improvement studies and other medical tasks. Recently, web and mobile technologies have been explored as means of improving the response rates and quality of data collected. Despite the potential benefit of this approach, there are currently no widely accepted standards for developing or implementing PROMs in CER (Comparative Effectiveness Research).

Feasibility and acceptability of TRANSFoRm to improve clinical trial recruitment in primary care.

Background: Recruitment of study participants is a challenging process for health professionals and patients. The Translational Medicine and Patient Safety in Europe (TRANSFoRm) clinical trial tools enable automated identification, recruitment and follow-up in clinical trials, potentially saving time, effort and costs for all parties involved.

Objectives: This study evaluates the acceptability and feasibility of TRANSFoRm to improve clinical trial recruitment in primary care.

Extraction of multilayered social networks from activity data.

The data gathered in all kinds of web-based systems, which enable users to interact with each other, provides an opportunity to extract social networks that consist of people and relationships between them. The emerging structures are very complex due to the number and type of discovered connections. In web-based systems, the characteristic element of each interaction between users is that there is always an object that serves as a communication medium.