Impact of community structure on information transfer

The observation that real complex networks have internal structure has important implication for dynamic processes occurring on such topologies. Here we investigate the impact of community structure on a model of information transfer able to deal with both search and congestion simultaneously. We show that networks with fuzzy community structure are more efficient in terms of packet delivery than those with pronounced community structure. We also propose an alternative packet routing algorithm which takes advantage of the knowledge of communities to improve information transfer and show that in the context of the model an intermediate level of community structure is optimal. Finally, we show that in a hierarchical network setting, providing knowledge of communities at the level of highest modularity will improve network capacity by the largest amount

Identificación de comunidades analizando el uso del correo electrónico

Durante la última década hemos asistido al uso generalizado del correo electrónico como herramienta de comunicación en nuestra sociedad. Su utilización dentro delas organizaciones no escapa a esa tendencia y buena parte del flujo de información interno de una compañía se realizade esta forma. La monitorización de su uso, preservando el anonimato de sus usuarios, se convierte en una herramienta muy valiosa para conocer la estructura informal de la organización y para compararla con la estructura formal. En particular presentamos en este trabajo el análisis de comunidades que se deduce de la red de correo de la Universitat Rovira i Virgili de Tarragona, España

Optimal Information Transmission in Organizations: Search and Congestion

We propose a stylized model of a problem-solving organization whose internal communication structure is given by a fixed network. Problems arrive randomly anywhere in this network and must find their way to their respective “specialized solvers” by relying on local information alone. The organization handles multiple problems simultaneously. For this reason, the process may be subject to congestion. We provide a characterization of the threshold of collapse of the network and of the stock of floating problems (or average delay) that prevails below that threshold. We build upon this characterization to address a design problem: the determination of what kind of network architecture optimizes performance for any given problem arrival rate. We conclude that, for low arrival rates, the optimal network is very polarized (i.e. star-like or “centralized”), whereas it is largely homogenous (or “decentralized”) for high arrival rates. We also show that, if an auxiliary assumption holds, the transition between these two opposite structures is sharp and they are the only ones to ever qualify as optimal. Keywords: Networks, information transmission, search, organization design.Networks, Information transmission, Search, Organization design

Degree of intervality of food webs: From body-size data to models

In food webs, the degree of intervality of consumers' diets is an indicator of the number of dimensions that are necessary to determine the niche of a species. Previous studies modeling food-web structure have shown that real networks are compatible with a high degree of diet contiguity. However, current models are also compatible with the opposite, namely that species' diets have relatively low contiguity. This is particularly true when one takes species' body size as a proxy for niche value, in which case the indeterminacy of diet contiguities provided by current models can be large. We propose a model that enables us to narrow down the range of possible values of diet contiguity. According to this model, we find that diet contiguity not only can be high, but must be high when species are ranked in ascending order of body size.This work was supported by a James S. Mc Donnell Foundation Research Award (R.G.), European Union Grant PIRG-GA-2010-277166 (R.G.), Spanish Ministerio de Ciencia e Innovación (MICINN) Grants FIS2009-13370-C02-01 A.A.), FIS2010-18639 (R.G.), PRODIEVO, and FIS2011-27569 (J.A.C.), Comunidad de Madrid Grant MODELICO-CM (J.A.C.) and by Generalitat de Catalunya 2009-SGR-838 (A.A.).Publicad

Optimal information transmission in organizations: Search and congestion

We propose a stylized model of a problem-solving organization whose internal communication structure is given by a fixed network. Problems arrive randomly anywhere in this network and must find their way to their respective “specialized solvers” by relying on local information alone. The organization handles multiple problems simultaneously. For this reason, the process may be subject to congestion. We provide a characterization of the threshold of collapse of the network and of the stock of foating problems (or average delay) that prevails below that threshold. We build upon this characterization to address a design problem: the determination of what kind of network architecture optimizes performance for any given problem arrival rate. We conclude that, for low arrival rates, the optimal network is very polarized (i.e. star-like or “centralized”), whereas it is largely homogenous (or “decentralized”) for high arrival rates. We also show that, if an auxiliary assumption holds, the transition between these two opposite structures is sharp and they are the only ones to ever qualify as optimal.Networks, information transmission, search, organization design

Xarxes completes: de la cèl·lula al Facebook

Les unitats que formen els sistemes complexos interaccionen entre elles en xarxes que no són ni perfectament regulars ni perfectament aleatòries. L'estructura d'aquestes xarxes determina el comportament dels processos dinàmics que hi tenen lloc i, alhora, és un refl ex de l'evolució i les funcions del sistema. Des d'aquesta doble perspec�� va, l'estudi de les xarxes complexes és important. En aquest ar�� cle discu�� m el resultats clàssics de la teoria de xarxes i algunes de les línies de recerca actualment més ac�� ves

Xarxes completes: de la cèl·lula al Facebook

Les unitats que formen els sistemes complexos interaccionen entre elles en xarxes que no són ni perfectament regulars ni perfectament aleatòries. L'estructura d'aquestes xarxes determina el comportament dels processos dinàmics que hi tenen lloc i, alhora, és un refl ex de l'evolució i les funcions del sistema. Des d'aquesta doble perspec�� va, l'estudi de les xarxes complexes és important. En aquest ar�� cle discu�� m el resultats clàssics de la teoria de xarxes i algunes de les línies de recerca actualment més ac�� ves

Emergence of assortative mixing between clusters of cultured neurons

The analysis of the activity of neuronal cultures is considered to be a good proxy of the functional connectivity of in vivo neuronal tissues. Thus, the functional complex network inferred from activity patterns is a promising way to unravel the interplay between structure and functionality of neuronal systems. Here, we monitor the spontaneous self-sustained dynamics in neuronal cultures formed by interconnected aggregates of neurons (clusters). Dynamics is characterized by the fast activation of groups of clusters in sequences termed bursts. The analysis of the time delays between clusters' activations within the bursts allows the reconstruction of the directed functional connectivity of the network. We propose a method to statistically infer this connectivity and analyze the resulting properties of the associated complex networks. Surprisingly enough, in contrast to what has been reported for many biological networks, the clustered neuronal cultures present assortative mixing connectivity values, meaning that there is a preference for clusters to link to other clusters that share similar functional connectivity, as well as a rich-club core, which shapes a"connectivity backbone" in the network. These results point out that the grouping of neurons and the assortative connectivity between clusters are intrinsic survival mechanisms of the culture

Synchronization invariance under network structural transformations

Synchronization processes are ubiquitous despite the many connectivity patterns that complex systems can show. Usually, the emergence of synchrony is a macroscopic observable; however, the microscopic details of the system, as, e.g., the underlying network of interactions, is many times partially or totally unknown. We already know that different interaction structures can give rise to a common functionality, understood as a common macroscopic observable. Building upon this fact, here we propose network transformations that keep the collective behavior of a large system of Kuramoto oscillators invariant. We derive a method based on information theory principles, that allows us to adjust the weights of the structural interactions to map random homogeneous in-degree networks into random heterogeneous networks and vice versa, keeping synchronization values invariant. The results of the proposed transformations reveal an interesting principle; heterogeneous networks can be mapped to homogeneous ones with local information, but the reverse process needs to exploit higher-order information. The formalism provides analytical insight to tackle real complex scenarios when dealing with uncertainty in the measurements of the underlying connectivity structure