Showing papers by "Albert-László Barabási published in 2012"
TL;DR: A stochastic process capturing local mobility decisions that helps to derive commuting and mobility fluxes that require as input only information on the population distribution is introduced, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.
Abstract: A parameter-free model predicts patterns of commuting, phone calls and trade using only population density at all intermediate points. Since the 1940s, planners needing to predict population movement, transport-network usage and even epidemics have turned to a model based on the 'gravity law'. This assumes that the number of individuals travelling between two locations is proportional to the population at the source and destination, and decays with distance. This approach has its limitations, because it looks at the flow between two specific points only. Here, Albert-Laszlo Barabasi and colleagues present an alternative model that takes into account population density at all intermediate points. Their parameter-free radiation model predicts a range of phenomena — from commuting and migrations to phone calls — much more accurately than the gravity model. Needing only data on population densities, which are easy to measure, the system can be used to predict commuting and transport patterns even in areas where data are not collected systematically. Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century2, the gravity law1,3,4 is the prevailing framework with which to predict population movement3,5,6, cargo shipping volume7 and inter-city phone calls8,9, as well as bilateral trade flows between nations10. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes11,12,13,14,15,16,17,18,19,20,21,22,23.
1,237 citations
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TL;DR: In this article, the authors propose a new discipline of network science, which is based on data-based mathematical models of complex systems, which are offering a fresh perspective, rapidly developing into network science.
Abstract: Reductionism, as a paradigm, is expired, and complexity, as a field, is tired. Data-based mathematical models of complex systems are offering a fresh perspective, rapidly developing into a new discipline: network science.
477 citations
TL;DR: It is shown that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations, to increase the specificity of cancer gene identification.
Abstract: Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.
339 citations
TL;DR: It is shown that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution for a broad class of phenomena.
Abstract: Inhomogeneous temporal processes, like those appearing in human communications, neuron spike trains, and seismic signals, consist of high-activity bursty intervals alternating with long low-activity periods. In recent studies such bursty behavior has been characterized by a fat-tailed inter-event time distribution, while temporal correlations were measured by the autocorrelation function. However, these characteristic functions are not capable to fully characterize temporally correlated heterogenous behavior. Here we show that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution for a broad class of phenomena. We find that the correlations in these quite different systems can be commonly interpreted by memory effects and described by a simple phenomenological model, which displays temporal behavior qualitatively similar to that in real systems.
317 citations
TL;DR: In this paper, the authors introduced the concept of control centrality to quantify the ability of a single node to control a directed weighted network and showed that it is mainly determined by the network's degree distribution.
Abstract: We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.
292 citations
TL;DR: In this article, microRNA-21 (miR-21) is predicted as a PH-modifying microRNA, regulating targets integral to bone morphogenetic protein (BMP) and Rho/Rho-kinase signaling as well as functional pathways associated with hypoxia, inflammation, and genetic haploinsufficiency of BMP receptor type 2.
Abstract: Background—Pulmonary hypertension (PH) is driven by diverse pathogenic etiologies. Owing to their pleiotropic actions, microRNA molecules are potential candidates for coordinated regulation of these disease stimuli. Methods and Results—Using a network biology approach, we identify microRNA associated with multiple pathogenic pathways central to PH. Specifically, microRNA-21 (miR-21) is predicted as a PH-modifying microRNA, regulating targets integral to bone morphogenetic protein (BMP) and Rho/Rho-kinase signaling as well as functional pathways associated with hypoxia, inflammation, and genetic haploinsufficiency of BMP receptor type 2. To validate these predictions, we have found that hypoxia and BMP receptor type 2 signaling independently upregulate miR-21 in cultured pulmonary arterial endothelial cells. In a reciprocal feedback loop, miR-21 downregulates BMP receptor type 2 expression. Furthermore, miR-21 directly represses RhoB expression and Rho-kinase activity, inducing molecular changes consistent...
254 citations
Journal Article•
TL;DR: Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, an efficient attack strategy is designed against the controllability of malicious networks.
Abstract: We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.
235 citations
TL;DR: Striking sex differences in the gender-bias of preferred relationships are demonstrated that reflect the way the reproductive investment strategies of both sexes change across the lifespan, i.e. women's shifting patterns of investment in reproduction and parental care.
Abstract: Social networks based on dyadic relationships are fundamentally important for understanding of human sociality. However, we have little understanding of the dynamics of close relationships and how these change over time. Evolutionary theory suggests that, even in monogamous mating systems, the pattern of investment in close relationships should vary across the lifespan when post-weaning investment plays an important role in maximising fitness. Mobile phone data sets provide a unique window into the structure and dynamics of relationships. We here use data from a large mobile phone dataset to demonstrate striking sex differences in the gender-bias of preferred relationships that reflect the way the reproductive investment strategies of both sexes change across the lifespan, i.e. women's shifting patterns of investment in reproduction and parental care. These results suggest that human social strategies may have more complex dynamics than previously assumed and a life-history perspective is crucial for understanding them.
126 citations
TL;DR: This study shows that popularity is a strong force in shaping complex network structure and dynamics, but so too is similarity, and develops a model that increases the accuracy of network-evolution predictions by considering the trade-offs between popularity and similarity.
Abstract: The concept of preferential attachment is behind the hubs and power laws seen in many networks. New results fuel an old debate about its origin, and beg the question of whether it is based on randomness or optimization. See Letter p.537
Preferential attachment is a mechanism that attempts to explain the emergence of scaling in growing networks. If new connections are preferentially established with more popular nodes in a network, then the network is scale-free. So, because 'popularity is attractive', does preferential attachment predict network evolution? This study shows that popularity is a strong force in shaping complex network structure and dynamics, but so too is similarity. The authors develop a model that increases the accuracy of network-evolution predictions by considering the trade-offs between popularity and similarity. The model accurately describes large-scale evolution of technological (Internet), social and metabolic networks, predicting the probability of new links with high precision.
113 citations
TL;DR: In this article, the authors examined whether viral perturbations of host interactome may underlie such virally implicated disease relationships, using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV).
Abstract: Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.
100 citations
TL;DR: A continuum theory is developed that not only predicts the stability of the ranking process, but shows that a noise-induced phase transition is at the heart of the observed differences in ranking regimes.
Abstract: The world is addicted to ranking: everything, from the reputation of scientists, journals, and universities to purchasing decisions is driven by measured or perceived differences between them. Here, we analyze empirical data capturing real time ranking in a number of systems, helping to identify the universal characteristics of ranking dynamics. We develop a continuum theory that not only predicts the stability of the ranking process, but shows that a noise-induced phase transition is at the heart of the observed differences in ranking regimes. The key parameters of the continuum theory can be explicitly measured from data, allowing us to predict and experimentally document the existence of three phases that govern ranking stability.
TL;DR: It is found that of all papers published in five leading journals in 1990, the most highly cited 1% in each collected around 17% of citations in 2010, a shift that may reflect the fact that, although the number of research papers has exploded, the time scientists devote to reading them has not.
Posted Content•
TL;DR: It is shown that the degree and link weight distributions in social networks can be expressed in terms of the dynamical exponents characterizing human activity patterns, and the validity of these theoretical predictions on datasets capturing various facets of human interactions is tested.
Abstract: The increasing availability of large-scale data on human behavior has catalyzed simultaneous advances in network theory, capturing the scaling properties of the interactions between a large number of individuals, and human dynamics, quantifying the temporal characteristics of human activity patterns. These two areas remain disjoint, each pursuing as separate lines of inquiry. Here we report a series of generic relationships between the quantities characterizing these two areas by demonstrating that the degree and link weight distributions in social networks can be expressed in terms of the dynamical exponents characterizing human activity patterns. We test the validity of these theoretical predictions on datasets capturing various facets of human interactions, from mobile calls to tweets.
TL;DR: In this article, the authors study the impact of various network characteristics on the minimal number of driver nodes required to control a network and find that clustering and modularity have no discernible impact, but the symmetries of the underlying matching problem can produce linear, quadratic or no dependence on degree correlation coefficients.
Abstract: A dynamical system is controllable if by imposing appropriate external signals on a subset of its nodes, it can be driven from any initial state to any desired state in finite time. Here we study the impact of various network characteristics on the minimal number of driver nodes required to control a network. We find that clustering and modularity have no discernible impact, but the symmetries of the underlying matching problem can produce linear, quadratic or no dependence on degree correlation coefficients, depending on the nature of the underlying correlations. The results are supported by numerical simulations and help narrow the observed gap between the predicted and the observed number of driver nodes in real networks.
Journal Article•
TL;DR: It is found that the social and organizational context significantly impacts to whom and how fast people forward information, yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context.
Abstract: Information spreading processes are central to human interactions. Despite recent studies in online domains, little is known about factors that could affect the dissemination of a single piece of information. In this paper, we address this challenge by combining two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system. We find that the social and organizational context significantly impacts to whom and how fast people forward information. Yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context. Our results build the foundation of future predictive models of information flow and provide significant insights towards design of communication platforms.
Proceedings Article•
19 Mar 2012TL;DR: To understand networks, CCNR's research has developed to rather unexpected areas, including the topology of the www - showing that webpages are on average 19 clicks form each other; complex cellular network inside the cell; the Internet's Achilles' Heel.
Abstract: The Center for Complex Network Research (CCNR), directed by Professor Barabasi, has a simple objective: think networks. The center's research focuses on how networks emerge, what they look like, and how they evolve; and how networks impact on understanding of complex systems. To understand networks, CCNR's research has developed to rather unexpected areas. Certain studies include the topology of the www - showing that webpages are on average 19 clicks form each other; complex cellular network inside the cell-looking at both metabolic and genetic networks; the Internet's Achilles' Heel. The center's researchers have even ventured to study how actors are connected in Hollywood.
01 Jan 2012
TL;DR: In this article, the authors use volatility and spacing to quantify the dynamics of a wide class of ranked systems and construct a model using stochastic differential equations to reproduce the salient features observed in the data.
Abstract: This dissertation uses volatility and spacing to allow one to quantify the dynamics of a wide class of ranked systems. The systems we consider are any set of items, each with an associated score that may change over time. We define volatility as the standard deviation of the score of an item. We define spacing as the distance in score from one item to its neighbor. From these two concepts we construct a model using stochastic differential equations. We measure the model parameters in a variety of ranked systems and use the model to reproduce the salient features observed in the data. We continue by constructing a spacing-volatility diagram that summarizes three unique stability phases and overlay each dataset on this diagram. We end by discussing limitations and extensions to such a model.
TL;DR: In this article, the authors used data from a large national mobile phone dataset to demonstrate striking sex differences in the pattern in the gender-bias of preferred relationships that reflect the way the reproductive investment strategies of the two sexes change across the lifespan.
Abstract: Social networks have turned out to be of fundamental importance both for our understanding human sociality and for the design of digital communication technology. However, social networks are themselves based on dyadic relationships and we have little understanding of the dynamics of close relationships and how these change over time. Evolutionary theory suggests that, even in monogamous mating systems, the pattern of investment in close relationships should vary across the lifespan when post-weaning investment plays an important role in maximising fitness. Mobile phone data sets provide us with a unique window into the structure of relationships and the way these change across the lifespan. We here use data from a large national mobile phone dataset to demonstrate striking sex differences in the pattern in the gender-bias of preferred relationships that reflect the way the reproductive investment strategies of the two sexes change across the lifespan: these differences mainly reflect women's shifting patterns of investment in reproduction and parental care. These results suggest that human social strategies may have more complex dynamics than we have tended to assume and a life-history perspective may be crucial for understanding them.