Network Geometry Inference using Common Neighbors

We introduce and explore a new method for inferring hidden geometric coordinates of nodes in complex networks based on the number of common neighbors between the nodes. We compare this approach to the HyperMap method, which is based only on the connections (and disconnections) between the nodes, i.e., on the links that the nodes have (or do not have). We find that for high degree nodes the common-neighbors approach yields a more accurate inference than the link-based method, unless heuristic periodic adjustments (or "correction steps") are used in the latter. The common-neighbors approach is computationally intensive, requiring $O(t^4)$ running time to map a network of $t$ nodes, versus $O(t^3)$ in the link-based method. But we also develop a hybrid method with $O(t^3)$ running time, which combines the common-neighbors and link-based approaches, and explore a heuristic that reduces its running time further to $O(t^2)$, without significant reduction in the mapping accuracy. We apply this method to the Autonomous Systems (AS) Internet, and reveal how soft communities of ASes evolve over time in the similarity space. We further demonstrate the method's predictive power by forecasting future links between ASes. Taken altogether, our results advance our understanding of how to efficiently and accurately map real networks to their latent geometric spaces, which is an important necessary step towards understanding the laws that govern the dynamics of nodes in these spaces, and the fine-grained dynamics of network connections

Closed benchmarks for network community structure characterization

Characterizing the community structure of complex networks is a key challenge in many scientific fields. Very diverse algorithms and methods have been proposed to this end, many working reasonably well in specific situations. However, no consensus has emerged on which of these methods is the best to use in practice. In part, this is due to the fact that testing their performance requires the generation of a comprehensive, standard set of synthetic benchmarks, a goal not yet fully achieved. Here, we present a type of benchmark that we call "closed", in which an initial network of known community structure is progressively converted into a second network whose communities are also known. This approach differs from all previously published ones, in which networks evolve toward randomness. The use of this type of benchmark allows us to monitor the transformation of the community structure of a network. Moreover, we can predict the optimal behavior of the variation of information, a measure of the quality of the partitions obtained, at any moment of the process. This enables us in many cases to determine the best partition among those suggested by different algorithms. Also, since any network can be used as a starting point, extensive studies and comparisons can be performed using a heterogeneous set of structures, including random ones. These properties make our benchmarks a general standard for comparing community detection algorithms.Comment: 18 pages, 5 figures. Available at http://pre.aps.org/abstract/PRE/v85/i2/e02610

Detección de comunidades en redes complejas

xiv, 142 p., figuras y material suplementario[EN] Networks have become a widely used tool for modeling complex systems in many di erent elds. This approach is extremely useful for representing interactions among genes, social relationships, Internet communications or correlations of prices within a stock market, to name just a few examples. By analyzing the structure of these networks and understanding how their di erent elements interact, we could improve our knowledge of the whole system. Usually, nodes that compose these networks tend to create tightly knit groups. This property, of high interest in many scienti c elds, is called community structure and improving its detection and characterization is what this thesis is all about. The rst objective of this work is the generation of e cient methods able to characterize the communities of a network and to understand its structure. Second, we will try to create a set of tests where such methods can be studied. Finally, we will suggest a statistical measure in order to be able to properly assess the quality of the community structure of a network. To accomplish these objectives, rst, we generate a set of algorithms that can transform a network into a hierarchical tree and, from there, to determine their most relevant communities. Furthermore, we have developed a new type of benchmarks for e ectively testing these and other community detection algorithms. Finally, and as the most important contribution of this work, it is shown that the community structure of a network can be accurately evaluated using a hypergeometric distribution-based index. Thus, the maximization of this measure, called Surprise, appears as the best proposed strategy for detecting the optimal partition into communities of a network. Surprise exhibits an excellent behavior in all networks analyzed, qualitatively outperforming any previous method. Thus, it appears as the best measure proposed to this end and the data suggests that it could be an optimal strategy to determine the quality of the community structure of complex networks.[ES] El uso de las redes para modelar sistemas complejos es creciente en multitud de ámbitos. Son extremadamente útiles para representar interacciones entre genes, relaciones sociales, intercambio de información en Internet o correlaciones entre precios de acciones bursátiles, por nombrar sólo algunos ejemplos. Analizando la estructura de estas redes, comprendiendo cómo interaccionan sus distintos elementos, podremos entender mejor cómo se comporta el sistema en su conjunto. A menudo, los nodos que conforman estas redes tienden a formar grupos altamente conectados. Esta propiedad es conocida como estructura de comunidades y esta tesis doctoral se ha centrado en el problema de cómo mejorar su detección y caracterización. Como primer objetivo de este trabajo, se encuentra la generación de m etodos e cientes que permitan caracterizar las comunidades de una red y comprender su estructura. Segundo, pretendemos plantear una serie de pruebas donde testar dichos m etodos. Por ultimo, sugeriremos una medida estad stica que pretende ser capaz de evaluar correctamente la calidad de la estructura de comunidades de una red. Para llevar a cabo dichos objetivos, en primer lugar, se generan una serie de algoritmos capaces de transformar una red en un arbol jer arquico y,a partir de ah , determinar las comunidades que aparecen en ella. Por otro lado,se ha dise~nado un nuevo tipo de benchmarks para testar estos y otros algoritmos de detecci on de comunidades de forma e ciente. Por ultimo, y como parte m as importante de este trabajo, se demuestra que la estructura de comunidades de una red puede ser correctamente evaluada utilizando una medida basada en una distribuci on hipergeom etrica. Por tanto, la maximizaci on de este ndice, llamado Surprise, aparece como la estrategia id onea para obtener la partici on en comunidades optima de una red. Surprise ha mostrado un comportamiento excelente en todos los casos analizados, superando cualitativamente a cualquier otro m etodo anterior. De esta manera, aparece como la mejor medida propuesta para este n y los datos sugieren que podr a ser una estrategia optima para determinar la calidad de la estructura de comunidades en redes complejas.Peer reviewe

An Experimental Investigation of Hyperbolic Routing with a Smart Forwarding Plane in NDN

Routing in NDN networks must scale in terms of forwarding table size and routing protocol overhead. Hyperbolic routing (HR) presents a potential solution to address the routing scalability problem, because it does not use traditional forwarding tables or exchange routing updates upon changes in network topologies. Although HR has the drawbacks of producing sub-optimal routes or local minima for some destinations, these issues can be mitigated by NDN's intelligent data forwarding plane. However, HR's viability still depends on both the quality of the routes HR provides and the overhead incurred at the forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's sub-optimal path selection. This paper describes our experimental investigation into the packet delivery delay and overhead under HR as compared with Named-Data Link State Routing (NLSR), which calculates shortest paths. We run emulation experiments using various topologies with different failure scenarios, probing intervals, and maximum number of next hops for a name prefix. Our results show that HR's delay stretch has a median close to 1 and a 95th-percentile around or below 2, which does not grow with the network size. HR's message overhead in dynamic topologies is nearly independent of the network size, while NLSR's overhead grows polynomially at least. These results suggest that HR offers a more scalable routing solution with little impact on the optimality of routing paths

Detección de comunidades en redes complejas

En este trabajo se presentan avances realizados en herramientas y modelos matemáticos para el análisis y detección de la estructura de comunidades de una red. Además de una serie de nuevos algoritmos, también se propone una medida de calidad de dicha estructura de comunidades, que supera cualitativamente a la medida más popular en la actualidad. Por último se presentan un nuevo tipo de benchmarks que aportan nuevas estrategias para caracterizar correctamente las comunidades de una red.Aldecoa García, R. (2012). Detección de comunidades en redes complejas. http://hdl.handle.net/10251/15337Archivo delegad

A sequence motif enriched in regions bound by the Drosophila dosage compensation complex

Abstract Background In Drosophila melanogaster, dosage compensation is mediated by the action of the dosage compensation complex (DCC). How the DCC recognizes the fly X chromosome is still poorly understood. Characteristic sequence signatures at all DCC binding sites have not hitherto been found. Results In this study, we compare the known binding sites of the DCC with oligonucleotide profiles that measure the specificity of the sequences of the D. melanogaster X chromosome. We show that the X chromosome regions bound by the DCC are enriched for a particular type of short, repetitive sequences. Their distribution suggests that these sequences contribute to chromosome recognition, the generation of DCC binding sites and/or the local spreading of the complex. Comparative data indicate that the same sequences may be involved in dosage compensation in other Drosophila species. Conclusions These results offer an explanation for the wild-type binding of the DCC along the Drosophila X chromosome, contribute to delineate the forces leading to the establishment of dosage compensation and suggest new experimental approaches to understand the precise biochemical features of the dosage compensation system.This work was supported by Ministerio de Ciencia e Innovación, Spain [grant number BIO2008-05067].Peer Reviewe

A sequence motif enriched in regions bound by the Drosophila dosage compensation complex

<p>Abstract</p> <p>Background</p> <p>In <it>Drosophila melanogaster</it>, dosage compensation is mediated by the action of the dosage compensation complex (DCC). How the DCC recognizes the fly X chromosome is still poorly understood. Characteristic sequence signatures at all DCC binding sites have not hitherto been found.</p> <p>Results</p> <p>In this study, we compare the known binding sites of the DCC with oligonucleotide profiles that measure the specificity of the sequences of the <it>D. melanogaster </it>X chromosome. We show that the X chromosome regions bound by the DCC are enriched for a particular type of short, repetitive sequences. Their distribution suggests that these sequences contribute to chromosome recognition, the generation of DCC binding sites and/or the local spreading of the complex. Comparative data indicate that the same sequences may be involved in dosage compensation in other <it>Drosophila </it>species.</p> <p>Conclusions</p> <p>These results offer an explanation for the wild-type binding of the DCC along the <it>Drosophila </it>X chromosome, contribute to delineate the forces leading to the establishment of dosage compensation and suggest new experimental approaches to understand the precise biochemical features of the dosage compensation system.</p

Deciphering Network Community Structure by Surprise

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.Comment: 7 pages, 5 figure

Espacio habitacional/espacio gráfico: grabados al aire libre en el término de La Hinojosa (Cuenca)

The work that now we present is based on the analysis of graphic designs, their location and their relationship to the environment, in the municipality of La Hinojosa Cuenca, a place where we have well documented occupations of the Copper and Bronze Ages. Our aim is to understand these engravings as part of the expressions of peninsular Schematic Art, in this case associated with an occupational context, and to provide elements for the comprehension of its message in a broader context that includes occupational contexts for other open aire engravings in the Iberian Peninsula.El trabajo que ahora presentamos se basa en el análisis de las grafías, su ubicación en el soporte y su relación con el entorno, en una zona concreta, el término de La Hinojosa, en Cuenca, lugar en el que tenemos bien documentadas ocupaciones calcolíticas y de la Edad del Bronce. Nuestra propuesta es la de comprender dichos grabados como una parte de las expresiones del Arte Esquemático peninsular, en este caso asociadas a un contexto habitacional y, a partir de ahí, aportar elementos para la comprensión de su "mensaje" desde una perspectiva más amplia que incluye otros contextos habitacionales para conjuntos de grabados al aire libre en la Península Ibérica