119 research outputs found
Missing and spurious interactions and the reconstruction of complex networks
Network analysis is currently used in a myriad of contexts: from identifying
potential drug targets to predicting the spread of epidemics and designing
vaccination strategies, and from finding friends to uncovering criminal
activity. Despite the promise of the network approach, the reliability of
network data is a source of great concern in all fields where complex networks
are studied. Here, we present a general mathematical and computational
framework to deal with the problem of data reliability in complex networks. In
particular, we are able to reliably identify both missing and spurious
interactions in noisy network observations. Remarkably, our approach also
enables us to obtain, from those noisy observations, network reconstructions
that yield estimates of the true network properties that are more accurate than
those provided by the observations themselves. Our approach has the potential
to guide experiments, to better characterize network data sets, and to drive
new discoveries
Modularity from Fluctuations in Random Graphs and Complex Networks
The mechanisms by which modularity emerges in complex networks are not well
understood but recent reports have suggested that modularity may arise from
evolutionary selection. We show that finding the modularity of a network is
analogous to finding the ground-state energy of a spin system. Moreover, we
demonstrate that, due to fluctuations, stochastic network models give rise to
modular networks. Specifically, we show both numerically and analytically that
random graphs and scale-free networks have modularity. We argue that this fact
must be taken into consideration to define statistically-significant modularity
in complex networks.Comment: 4 page
Robust Patterns in Food Web Structure
We analyze the properties of seven community food webs from a variety of
environments--including freshwater, marine-freshwater interfaces and
terrestrial environments. We uncover quantitative unifying patterns that
describe the properties of the diverse trophic webs considered and suggest that
statistical physics concepts such as scaling and universality may be useful in
the description of ecosystems. Specifically, we find that several quantities
characterizing these diverse food webs obey functional forms that are universal
across the different environments considered. The empirical results are in
remarkable agreement with the analytical solution of a recently proposed model
for food webs.Comment: 4 pages. Final version to appear in PR
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 âspecialized solversâ 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 âcentralizedâ), whereas it is largely homogenous (or âdecentralizedâ) 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
Justice Blocks and Predictability of U.S. Supreme Court Votes
Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions, we use models and methods that have been developed to uncover hidden associations between actors in complex social networks. We show that these methods are more accurate at predicting justice's votes than forecasts made by legal experts and by algorithms that take into consideration the content of the cases. We argue that, within our framework, high predictability is a quantitative proxy for stable justice (and case) blocks, which probably reflect stable a priori attitudes toward the law. We find that U.S. Supreme Court justice votes are more predictable than one would expect from an ideal court composed of perfectly independent justices. Deviations from ideal behavior are most apparent in divided 5â4 decisions, where justice blocks seem to be most stable. Moreover, we find evidence that justice predictability decreased during the 50-year period spanning from the Warren Court to the Rehnquist Court, and that aggregate court predictability has been significantly lower during Democratic presidencies. More broadly, our results show that it is possible to use methods developed for the analysis of complex social networks to quantitatively investigate historical questions related to political decision-making
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 âspecialized solversâ 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 âcentralizedâ), whereas it is largely homogenous (or âdecentralizedâ) 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
Module identification in bipartite and directed networks
Modularity is one of the most prominent properties of real-world complex
networks. Here, we address the issue of module identification in two important
classes of networks: bipartite networks and directed unipartite networks. Nodes
in bipartite networks are divided into two non-overlapping sets, and the links
must have one end node from each set. Directed unipartite networks only have
one type of nodes, but links have an origin and an end. We show that directed
unipartite networks can be conviniently represented as bipartite networks for
module identification purposes. We report a novel approach especially suited
for module detection in bipartite networks, and define a set of random networks
that enable us to validate the new approach
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