Navigation and Cognition in Semantic Networks

Semantic memory is the cognitive system devoted to storage and retrieval of conceptual knowledge. Empirical data indicate that semantic memory is organized in a network structure. Everyday experience shows that word search and retrieval processes emerge providing fluent and coherent speech, i.e. are efficient and robust. Nonetheless, links between pairs of words in semantic memory encode a rich variety of relationships, and not merely category membership. To extract this information, we schematize a process based on uncorrelated random walks from node to node, which converge to a feature vectors network. This mechanism forces the emergence of semantic similarity, which implicitly encloses category structure. Interestingly, the degradation of the original structure has a dramatic impact on the topology of semantic network, whereas the dynamics upon it evidence much higher resilience. We define this problem in the framework of percolation theory

Modeling self-sustained activity cascades in socio-technical networks

The ability to understand and eventually predict the emergence of information and activation cascades in social networks is core to complex socio-technical systems research. However, the complexity of social interactions makes this a challenging enterprise. Previous works on cascade models assume that the emergence of this collective phenomenon is related to the activity observed in the local neighborhood of individuals, but do not consider what determines the willingness to spread information in a time-varying process. Here we present a mechanistic model that accounts for the temporal evolution of the individual state in a simplified setup. We model the activity of the individuals as a complex network of interacting integrate-and-fire oscillators. The model reproduces the statistical characteristics of the cascades in real systems, and provides a framework to study time-evolution of cascades in a state-dependent activity scenario.Comment: 5 pages, 3 figure

The Contagion Effects of Repeated Activation in Social Networks

Demonstrations, protests, riots, and shifts in public opinion respond to the coordinating potential of communication networks. Digital technologies have turned interpersonal networks into massive, pervasive structures that constantly pulsate with information. Here, we propose a model that aims to analyze the contagion dynamics that emerge in networks when repeated activation is allowed, that is, when actors can engage recurrently in a collective effort. We analyze how the structure of communication networks impacts on the ability to coordinate actors, and we identify the conditions under which large-scale coordination is more likely to emerge.Comment: Submitted for publicatio

Using Twitter to Understand Public Interest in Climate Change: The case of Qatar

Climate change has received an extensive attention from public opinion in the last couple of years, after being considered for decades as an exclusive scientific debate. Governments and world-wide organizations such as the United Nations are working more than ever on raising and maintaining public awareness toward this global issue. In the present study, we examine and analyze Climate Change conversations in Qatar's Twittersphere, and sense public awareness towards this global and shared problem in general, and its various related topics in particular. Such topics include but are not limited to politics, economy, disasters, energy and sandstorms. To address this concern, we collect and analyze a large dataset of 109 million tweets posted by 98K distinct users living in Qatar -- one of the largest emitters of CO2 worldwide. We use a taxonomy of climate change topics created as part of the United Nations Pulse project to capture the climate change discourse in more than 36K tweets. We also examine which topics people refer to when they discuss climate change, and perform different analysis to understand the temporal dynamics of public interest toward these topics.Comment: Will appear in the proceedings of the International Workshop on Social Media for Environment and Ecological Monitoring (SWEEM'16

The joint influence of competition and mutualism on the biodiversity of mutualistic ecosystems

Relations among species in ecosystems can be represented by complex networks where both negative (competition) and positive (mutualism) interactions are concurrently present. Recently, it has been shown that many ecosystems can be cast into mutualistic networks, and that nestedness reduces effective inter-species competition, thus facilitating mutually beneficial interactions and increasing the number of coexisting species or the biodiversity. However, current approaches neglect the structure of inter-species competition by adopting a mean-field perspective that does not deal with competitive interactions properly. Here, we introduce a framework based on the concept of multilayer networks, which naturally accounts for both mutualism and competition. Hence, we abandon the mean field hypothesis and show, through a dynamical population model and numerical simulations, that there is an intricate relation between competition and mutualism. Specifically, we show that when all interactions are taken into account, mutualism does not have the same consequences on the evolution of specialist and generalist species. This leads to a non-trivial profile of biodiversity in the parameter space of competition and mutualism. Our findings emphasize how the simultaneous consideration of positive and negative interactions can contribute to our understanding of the delicate trade-offs between topology and biodiversity in ecosystems and call for a reconsideration of previous findings in theoretical ecology, as they may affect the structural and dynamical stability of mutualistic systems.Comment: 11 pages. Submitted for publicatio

Antagonistic Structural Patterns in Complex Networks

Identifying and explaining the structure of complex networks at different scales has become an important problem across disciplines. At the mesoscale, modular architecture has attracted most of the attention. At the macroscale, other arrangements --e.g. nestedness or core-periphery-- have been studied in parallel, but to a much lesser extent. However, empirical evidence increasingly suggests that characterizing a network with a unique pattern typology may be too simplistic, since a system can integrate properties from distinct organizations at different scales. Here, we explore the relationship between some of those organizational patterns: two at the mesoscale (modularity and in-block nestedness); and one at the macroscale (nestedness). We analytically show that nestedness can be used to provide approximate bounds for modularity, with exact results in an idealized scenario. Specifically, we show that nestedness and modularity are antagonistic. Furthermore, we evince that in-block nestedness provides a parsimonious transition between nested and modular networks, taking properties of both. Far from a mere theoretical exercise, understanding the boundaries that discriminate each architecture is fundamental, to the extent modularity and nestedness are known to place heavy constraints on the stability of several dynamical processes, specially in ecology.Comment: 7 pages, 4 figures and 1 supplemental information fil

Discrete-time Markov chain approach to contact-based disease spreading in complex networks

Many epidemic processes in networks spread by stochastic contacts among their connected vertices. There are two limiting cases widely analyzed in the physics literature, the so-called contact process (CP) where the contagion is expanded at a certain rate from an infected vertex to one neighbor at a time, and the reactive process (RP) in which an infected individual effectively contacts all its neighbors to expand the epidemics. However, a more realistic scenario is obtained from the interpolation between these two cases, considering a certain number of stochastic contacts per unit time. Here we propose a discrete-time formulation of the problem of contact-based epidemic spreading. We resolve a family of models, parameterized by the number of stochastic contact trials per unit time, that range from the CP to the RP. In contrast to the common heterogeneous mean-field approach, we focus on the probability of infection of individual nodes. Using this formulation, we can construct the whole phase diagram of the different infection models and determine their critical properties.Comment: 6 pages, 4 figures. Europhys Lett (in press 2010