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- Dmitri V. Krioukov, Fragkiskos Papadopoulos, Maksim Kitsak, Amin Vahdat, Marián Boguñá
- Physical review. E, Statistical, nonlinear, and…
- 2010

We develop a geometric framework to study the structure and function of complex networks. We assume that hyperbolic geometry underlies these networks, and we show that with this assumption, heterogeneous degree distributions and strong clustering in complex networks emerge naturally as simple reflections of the negative curvature and metric property of the… (More)

We review the behavior of epidemic spreading on complex networks in which there are explicit correlations among the degrees of connected vertices.

- Fragkiskos Papadopoulos, Marián Boguñá, Dmitri V. Krioukov
- Nature
- 2012

The principle that 'popularity is attractive' underlies preferential attachment, which is a common explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting distribution of the number of connections possessed by nodes follows power laws, as observed in many real… (More)

- Marián Boguñá, Romualdo Pastor-Satorras
- Physical review. E, Statistical, nonlinear, and…
- 2003

We study a class of models of correlated random networks in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices. We find analytical expressions for the main topological properties of these models as a function of the distribution of hidden variables and the probability of connecting vertices.… (More)

- Marián Boguñá, Romualdo Pastor-Satorras, Albert Díaz-Guilera, Alex Arenas
- Physical review. E, Statistical, nonlinear, and…
- 2004

We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by… (More)

- Fragkiskos Papadopoulos, Dmitri V. Krioukov, Marián Boguñá, Amin Vahdat
- INFOCOM
- 2010

—We show that complex (scale-free) network topolo-gies naturally emerge from hyperbolic metric spaces. Hyperbolic geometry facilitates maximally efficient greedy forwarding in these networks. Greedy forwarding is topology-oblivious. Nevertheless , greedy packets find their destinations with 100% probability following almost optimal shortest paths. This… (More)

PageRank has become a key element in the success of search engines, allowing to rank the most important hits in the top screen of results. One key aspect that distinguishes PageR-ank from other prestige measures such as in-degree is its global nature. From the information provider perspective, this makes it difficult or impossible to predict how their pages… (More)

- Marián Boguñá, Dmitri V. Krioukov, Kimberly C. Claffy
- ArXiv
- 2007

Routing information through networks is a universal phenomenon in both natural and manmade complex systems. When each node has full knowledge of the global network connectivity, finding short communication paths is merely a matter of distributed computation. However, in many real networks nodes communicate efficiently even without such global intelligence.… (More)

- Ma Angeles Serrano, Marián Boguñá
- Physical review. E, Statistical, nonlinear, and…
- 2003

Economy, and consequently trade, is a fundamental part of human social organization which, until now, has not been studied within the network modeling framework. Here we present the first, to the best of our knowledge, empirical characterization of the world trade web, that is, the network built upon the trade relationships between different countries in… (More)

- Michele Catanzaro, Marián Boguñá, Romualdo Pastor-Satorras
- Physical review. E, Statistical, nonlinear, and…
- 2005

Uncorrelated random scale-free networks are useful null models to check the accuracy and the analytical solutions of dynamical processes defined on complex networks. We propose and analyze a model capable of generating random uncorrelated scale-free networks with no multiple and self-connections. The model is based on the classical configuration model, with… (More)