182 research outputs found
Spectral goodness of fit for network models
We introduce a new statistic, 'spectral goodness of fit' (SGOF) to measure
how well a network model explains the structure of an observed network. SGOF
provides an absolute measure of fit, analogous to the standard R-squared in
linear regression. Additionally, as it takes advantage of the properties of the
spectrum of the graph Laplacian, it is suitable for comparing network models of
diverse functional forms, including both fitted statistical models and
algorithmic generative models of networks. After introducing, defining, and
providing guidance for interpreting SGOF, we illustrate the properties of the
statistic with a number of examples and comparisons to existing techniques. We
show that such a spectral approach to assessing model fit fills gaps left by
earlier methods and can be widely applied
Communication Network Design: Balancing Modularity and Mixing via Optimal Graph Spectra
By leveraging information technologies, organizations now have the ability to
design their communication networks and crowdsourcing platforms to pursue
various performance goals, but existing research on network design does not
account for the specific features of social networks, such as the notion of
teams. We fill this gap by demonstrating how desirable aspects of
organizational structure can be mapped parsimoniously onto the spectrum of the
graph Laplacian allowing the specification of structural objectives and build
on recent advances in non-convex programming to optimize them. This design
framework is general, but we focus here on the problem of creating graphs that
balance high modularity and low mixing time, and show how "liaisons" rather
than brokers maximize this objective
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Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions
Strategyproof mechanisms provide robust equilibrium
with minimal assumptions about knowledge and rationality but can be unachievable in combination with other desirable properties such as budget-balance, stability against deviations by coalitions, and computational tractability. In the search for maximally-strategyproof mechanisms
that simultaneously satisfy other desirable properties,
we introduce a new metric to quantify the strategyproofness of a mechanism, based on comparing the payoff distribution, given truthful reports, against that of a strategyproof “reference” mechanism that solves a problem relaxation.
Focusing on combinatorial exchanges, we demonstrate that the metric is informative about the eventual equilibrium, where simple regret-based metrics are not, and can be used for online selection of an effective mechanism.Engineering and Applied Science
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Approximate Strategyproofness
The standard approach of mechanism design theory insists on equilibrium behavior by participants. This assumption is captured by imposing incentive constraints on the design space. But in bridging from theory to practice, it often becomes necessary to relax incentive constraints in order to allow tradeoffs with other desirable properties. This article surveys a number of different options that can be adopted in relaxing incentive constraints, providing a current view of the state-of-the-art.Engineering and Applied Science
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A Collaborative Approach to Newspaper Layout
Engineering and Applied Science
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