Search results for "Complex network"

Betweenness Centrality for Networks with Non-Overlapping Community Structure

2018

Evaluating the centrality of nodes in complex networks is one of the major research topics being explored due to its wide range of applications. Among the various measures that have been developed over the years, Betweenness centrality is one of the most popular. Indeed, it has proved to be efficient in many real-world situations. In this paper, we propose an extension of the Betweenness centrality designed for networks with nonoverlapping community structure. It is a linear combination of the so-called “local” and “global” Betweenness measures. The Local measure takes into account the influence of a node at the community level while the global measure depends only on the interactions betwe…

2020

Hierarchy and centrality are two popular notions used to characterize the importance of entities in complex systems. Indeed, many complex systems exhibit a natural hierarchical structure, and centrality is a fundamental characteristic allowing to identify key constituents. Several measures based on various aspects of network topology have been proposed in order to quantify these concepts. While numerous studies have investigated whether centrality measures convey redundant information, how centrality and hierarchy measures are related is still an open issue. In this paper, we investigate the association between centrality and hierarchy using several correlation and similarity evaluation mea…

Complex network analysis of resting-state fMRI of the brain.

2016

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation …

Complex Networks in Air Transport

2016

The application of CNT to air traffic management has seen significant growth in recent years. This is partly because air traffic can be seen as the superposition of different networks, including the networks of airports, sectors and navigation points. Moreover each of these networks can be seen as a multiplex – for example, by associating each layer with a different airline. The study of the topology of these networks is important for several reasons related to understanding, monitoring, controlling, and optimising the air traffic system. The topological properties of air traffic networks are useful: (i) for studying how the air traffic has changed in recent years; (ii) for identifying the …

An approach for influence estimatation in complex networks : application to the social network Twitter

2019

Influence in complex networks and in particular Twitter has become recently a hot research topic. Detecting most influential users leads to reach a large-scale information diffusion area at low cost, something very useful in marketing or political campaigns. In this thesis, we propose a new approach that considers the several relations between users in order to assess influence in complex networks such as Twitter. We model Twitter as a multiplex heterogeneous network where users, tweets and objects are represented by nodes, and links model the different relations between them (e.g., retweets, mentions, and replies).The multiplex PageRank is applied to data from two datasets in the political…

Development of multivariate and network models for the analysis of Big Data: applications in economics, insurance, and social sciences

2020

In questa tesi sviluppo metodi statistici multivariati e di rete per lo studio di sistemi complessi. In particolare, focalizzo la mia analisi sullo studio di reti complesse bipartite e le loro applicazioni a (i) l'economia, per capire l'effetto di contagio tra istituti finanziari e stati sovrani, (ii) la sorveglianza nelle assicurazioni, per individuare comportamenti fraudolenti, e (iii) le scienze sociali, per studiare l'effetto delle politiche del REF sulle eccellenze nella ricerca delle università in UK. In this thesis I develop multivariate statistical and network methods for the study of complex systems. In particular, I focus my analysis on the study of bipartite complex networks and …

Choosing Optimal Seed Nodes in Competitive Contagion.

2019

International audience; In recent years there has been a growing interest in simulating competitive markets to find out the efficient ways to advertise a product or spread an ideology. Along this line, we consider a binary competitive contagion process where two infections, A and B, interact with each other and diffuse simultaneously in a network. We investigate which is the best centrality measure to find out the seed nodes a company should adopt in the presence of rivals so that it can maximize its influence. These nodes can be used as the initial spreaders or advertisers by firms when two firms compete with each other. Each node is assigned a price tag to become an initial advertiser whi…

Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

2017

Cellular functions are managed by a complex network of protein interactions, the malfunction of which may derive in disease phenotypes. In spite of the incompleteness and noise present in our current protein interaction maps, computational biologists are making strenuous efforts to extract knowledge from these intricate networks and, through their integration with other types of biological data, expedite the development of novel and more effective treatments against human disorders. The 3rd Challenges in Computational Biology meeting revolved around the Protein Interaction Networks and Disease subject, bringing expert network biologists to the city of Mainz, Germany to debate the current st…

Investigating Centrality Measures in Social Networks with Community Structure

2021

Centrality measures are crucial in quantifying the influence of the members of a social network. Although there has been a great deal of work dealing with this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world complex networks. In this paper, we investigate the interactions between 5 popular classical centrality measures and 5 community-aware centrality measures using 8 real-world online networks. Correlation as well as similarity measures between both t…

A Review of Mathematical and Computational Methods in Cancer Dynamics.

2022

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the si…