Ubiquitousness of link-density and link-pattern communities in real-world networks

Community structure appears to be an intrinsic property of many complex real-world networks. However, recent work shows that real-world networks reveal even more sophisticated modules than classical cohesive (link-density) communities. In particular, networks can also be naturally partitioned according to similar patterns of connectedness among the nodes, revealing link-pattern communities. We here propose a propagation based algorithm that can extract both link-density and link-pattern communities, without any prior knowledge of the true structure. The algorithm was first validated on different classes of synthetic benchmark networks with community structure, and also on random networks. We have further applied the algorithm to different social, information, technological and biological networks, where it indeed reveals meaningful (composites of) link-density and link-pattern communities. The results thus seem to imply that, similarly as link-density counterparts, link-pattern communities appear ubiquitous in nature and design

On the evolution of hyperlinking

Across time, the hyperlink object has supported different applications and studies. This is one perspective on the evolution of the hyperlinking concept, its context and related behaviors. Through a spectrum of hyperlinking applications and practices, the article contrasts the status quo with its related, broader, conceptual roots; it also bridges to some theorized and prototyped hyperlink variations, namely "stigmergic hyperlinks", to make the case that the ubiquitousness of some objects and certain usage patterns can obfuscate opportunities to (re)think them. In trying to contribute an answer to "what has the common hyperlink (such an apparently simple object) done to society, and what has society done to it?", the article identifies situations that have become so embedded in the daily routine, that it is now hard to think of hyperlinking alternatives.info:eu-repo/semantics/publishedVersio

Software systems through complex networks science: Review, analysis and applications

Complex software systems are among most sophisticated human-made systems, yet only little is known about the actual structure of 'good' software. We here study different software systems developed in Java from the perspective of network science. The study reveals that network theory can provide a prominent set of techniques for the exploratory analysis of large complex software system. We further identify several applications in software engineering, and propose different network-based quality indicators that address software design, efficiency, reusability, vulnerability, controllability and other. We also highlight various interesting findings, e.g., software systems are highly vulnerable to processes like bug propagation, however, they are not easily controllable

Self-similar scaling of density in complex real-world networks

Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within the physics literature. Here we investigate the scaling of density in complex networks under two classical box-covering renormalizations-network coarse-graining-and also different community-based renormalizations. The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin. The results thus advance a recent discovery of a universal scaling of density among different real-world networks [Laurienti et al., Physica A 390 (20) (2011) 3608-3613.] and imply an existence of a scale-free density also within-among different self-similar scales of-complex real-world networks. The latter further improves the comprehension of self-similar structure in large real-world networks with several possible applications

A detailed characterization of complex networks using Information Theory

Understanding the structure and the dynamics of networks is of paramount importance for manyscientific fields that rely on network science. Complex network theory provides a variety of features thathelp in the evaluation of network behavior. However, such analysis can be confusing and misleading asthere are many intrinsic properties for each network metric. Alternatively, Information Theory methodshave gained the spotlight because of their ability to create a quantitative and robust characterizationof such networks. In this work, we use two Information Theory quantifiers, namely Network Entropyand Network Fisher Information Measure, to analyzing those networks. Our approach detects nontrivialcharacteristics of complex networks such as the transition present in the Watts-Strogatz modelfrom k-ring to random graphs; the phase transition from a disconnected to an almost surely connectednetwork when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-freenetworks when considering a non-linear preferential attachment, fitness, and aging features alongsidethe configuration model with a pure power-law degree distribution. Finally, we analyze the numericalresults for real networks, contrasting our findings with traditional complex network methods. Inconclusion, we present an efficient method that ignites the debate on network characterization.Fil: Freitas, Cristopher G. S.. Universidade Federal de Alagoas; BrasilFil: Aquino, Andre L. L.. Universidade Federal de Alagoas; BrasilFil: Ramos, Heitor S.. Universidade Federal de Minas Gerais; BrasilFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Aníbal. Instituto Universitario del Hospital Italiano de Buenos Aires; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin