1,126 research outputs found
Networks become navigable as nodes move and forget
We propose a dynamical process for network evolution, aiming at explaining
the emergence of the small world phenomenon, i.e., the statistical observation
that any pair of individuals are linked by a short chain of acquaintances
computable by a simple decentralized routing algorithm, known as greedy
routing. Previously proposed dynamical processes enabled to demonstrate
experimentally (by simulations) that the small world phenomenon can emerge from
local dynamics. However, the analysis of greedy routing using the probability
distributions arising from these dynamics is quite complex because of mutual
dependencies. In contrast, our process enables complete formal analysis. It is
based on the combination of two simple processes: a random walk process, and an
harmonic forgetting process. Both processes reflect natural behaviors of the
individuals, viewed as nodes in the network of inter-individual acquaintances.
We prove that, in k-dimensional lattices, the combination of these two
processes generates long-range links mutually independently distributed as a
k-harmonic distribution. We analyze the performances of greedy routing at the
stationary regime of our process, and prove that the expected number of steps
for routing from any source to any target in any multidimensional lattice is a
polylogarithmic function of the distance between the two nodes in the lattice.
Up to our knowledge, these results are the first formal proof that navigability
in small worlds can emerge from a dynamical process for network evolution. Our
dynamical process can find practical applications to the design of spatial
gossip and resource location protocols.Comment: 21 pages, 1 figur
A Game Theoretic Model for the Formation of Navigable Small-World Networks
Kleinberg proposed a family of small-world networks to ex-plain the navigability of large-scale real-world social net-works. However, the underlying mechanism that drives real networks to be navigable is not yet well understood. In this paper, we present a game theoretic model for the for-mation of navigable small world networks. We model the network formation as a game in which people seek for both high reciprocity and long-distance relationships. We show that the navigable small-world network is a Nash Equilib-rium of the game. Moreover, we prove that the navigable small-world equilibrium tolerates collusions of any size and arbitrary deviations of a large random set of nodes, while non-navigable equilibria do not tolerate small group collu-sions or random perturbations. Our empirical evaluation further demonstrates that the system always converges to the navigable network even when limited or no information about other players ’ strategies is available. Our theoretical and empirical analyses provide important new insight on the connection between distance, reciprocity and navigability in social networks
Adaptive Dynamics of Realistic Small-World Networks
Continuing in the steps of Jon Kleinberg's and others celebrated work on
decentralized search in small-world networks, we conduct an experimental
analysis of a dynamic algorithm that produces small-world networks. We find
that the algorithm adapts robustly to a wide variety of situations in realistic
geographic networks with synthetic test data and with real world data, even
when vertices are uneven and non-homogeneously distributed.
We investigate the same algorithm in the case where some vertices are more
popular destinations for searches than others, for example obeying power-laws.
We find that the algorithm adapts and adjusts the networks according to the
distributions, leading to improved performance. The ability of the dynamic
process to adapt and create small worlds in such diverse settings suggests a
possible mechanism by which such networks appear in nature
Creating and navigating social and classroom spaces with gravity
Gravity is one the principle forces in the universe, its power always apparent, giving us three-dimensional creatures a constant sense of “up” and “down”. We propose the use of a metric for applying gravity, or similar “pulling” forces, to social environments by weighting and reordering set network structures where links cannot be added, but nodes may be rearranged. We begin by introducing gravity in social networks, describe previous web applications and uses, and then briefly experiment with the metric within a classroom setting. To that point, we describe and design requirements to effectively apply our metric to classrooms, as well as other social spaces. Finally, we assert that by flavoring network structures with our so-called “gravity”, we make those structures inherently more navigable in terms of personality similarity, and perhaps indirectly, communication and learning
GUARDIANS final report
Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a
large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we
discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with
the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings
A Global Repository for Planet-Sized Experiments and Observations
Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP)—output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.This work was supported by the U.S. Department of Energy Office of Science/Office of Biological and Environmental Research under Contract DE-AC52-07NA27344 at Lawrence Livermore National Laboratory. VB is supported by the Cooperative Institute for Climate Science, Princeton University, under Award NA08OAR4320752 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. Part of this work was undertaken with the
assistance of resources from the National Computational
Infrastructure (NCI), which is supported by the Australian Government. Part of this activity was performed on behalf of the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Part of this activity was performed on behalf of the Goddard Space Flight Center, under a contract with NASA. This work was supported by ANR Convergence project (Grant Agreement ANR-13-MONU-0008). This work was supported by FP7 IS-ENES2 project (Grant Agreement 312979)
A multi-task analysis and modelling paradigm using LSTM for multi-source monitoring data of inland vessels
The vessel monitoring data provide important information for people to understand the vessel dynamic status in real time and make appropriate decisions in vessel management and operations. However, some of the essential data may be incomplete or unavailable. In order to recover or predict the missing information and best exploit the vessels monitoring data, this paper combines statistical analysis, data mining and neural network methods to propose a multi-task analysis and modelling framework for multi-source monitoring data of inland vessels. Specifically, an advanced neural network, Long Short-Term Memory (LSTM) was tailored and employed to tackle three important tasks, including vessel trajectory repair, engine speed modelling and fuel consumption prediction. The developed models have been validated using the real-life vessel monitoring data and shown to outperform some other widely used modelling methods. In addition, statistics and data technologies were employed for data extraction, classification and cleaning, and an algorithm was designed for identification of the vessel navigational state
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