In this exploratory study, we used the community detection approach to complex networks analysis to analyze temperamental and executive functioning profiles of media multitaskers in early adolescence. Media multitasking is particularly intense in adolescence (Smahel et al., 2020), with implications for short- and long-term functioning (van der Schuur et al., 2015, 2020). Temperament and…
The critical node detection problem is a central task in computational graph theory due to its large applicability, consisting in deleting $k$ nodes to minimize a certain graph measure. In this article, we propose a new Extremal Optimization-based approach, the Pseudo-Deterministic Noisy Extremal Optimization (PDNEO) algorithm, to solve the Critical Node Detection variant in…
Social network analysis is a rapidly evolving research area having several real-life application areas, e.g. digital marketing, epidemiology, spread of misinformation. Influence maximization aims to select a subset of nodes in such manner that the information propagated over the network is maximized. Competitive influence maximization, which describes the phenomena of multiple actors competing for…
Network motifs in complex networks signify critical patterns of connections essential for deciphering system dynamics. Identifying and understanding these rare and elusive motifs is crucial for analyzing complex network behaviors. Our previous research has established a significant positive correlation between the occurrence of motifs and two network properties at the micro level, namely Assortativity…
The Influence Maximization Problem is a challenging computational task with multiple real-world applications. A new approach to this problem based on cooperative game theory and optimization called the Shapley Influence Maximization Extremal Optimization approach is proposed. The influence maximization problem for the independent cascade model is considered as a cooperative game, where players seek…
This paper proposes the use of Complex Network Theory to model the interactions between ants and analyze their social behavior. Specifically, the study focuses on six colonies of ants to investigate whether their behavior is community-oriented or individual-oriented. The research employs various nodes properties that define nodes’ importance to quantify the existence of a…
The critical node detection is a computational challenging problem with several applications in biology, sociology, etc. Minimizing the pairwise connectivity after removing k critical nodes is one of the most studied problem. In this paper we approach this problem by using a standard Extremal Optimization algorithm, and another variant with incorporated network shifting mechanism.…
Network science is emerging as a vibrant research field with important applications in finance, biology, chemistry, physics, engineering and health. This short paper presents an overview of some challenging tasks related to the analysis of complex networks, including community detection, discovery of cycles and identification of important nodes. The solutions proposed for these important…
The critical node detection problem describes a class of graph problems that involves identifying sets of nodes that influence a given graph metric. One variant of this problem is to find the nodes that – when removed from the graph – maximize the number of connected components in the remaining graph. This is an…
Determining the critical nodes in a network given a certain network measure is a computational challenging problem that requires the design of adaptive and scalable algorithms. The number of connected components in a graph is an example of such a measure: in this case the nodes considered critical are those that, if removed from…
