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delayed a bit by pilgramages to ASA/Chicago and Zingermans/Ann Arbor


   Barry Wellman
  _______________________________________________________________________
   FRSC                 INSNA Founder               University of Toronto
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED:The New Social Operating System.  Lee Rainie & Barry Wellman
   MIT Press            http://amzn.to/zXZg39        Print $14  Kindle $9
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 24 Aug 2015 11:04:04 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a80376167f&e=55e25a0e3e


Fundamental limitations of network reconstruction

    Network reconstruction helps us understand, diagnose and control complex networked systems by inferring properties of their interaction matrices, which characterize how nodes in the systems directly interact with each other. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations on which properties of the interaction matrix can be inferred from accessing the dynamics of individual nodes remain unknown. Here we characterize these fundamental limitations by deriving the necessary and sufficient condition to reconstruct any property of the interaction matrix. Counterintuitively, we prove that inferring less information ---such as the sign/connectivity pattern or the degree sequence--- does not make the network reconstruction problem easier than recovering the interaction matrix itself (i.e. the traditional parameter identification problem). Our analysis also reveals that using prior information of the
interaction matrix ---such as bound on the edge-weights--- is the only way to circumvent these fundamental limitations of network reconstruction. This sheds light on designing new algorithms with practical improvements over parameter identification methods.

Fundamental limitations of network reconstruction
Marco Tulio Angulo, Jaime A. Moreno, Albert-László Barabási, Yang-Yu Liu

http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=72408a9395&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=74e81f48bb&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=093fbf8b82&e=55e25a0e3e)



Supersampling and Network Reconstruction of Urban Mobility

    Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess
the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required.

Sagarra O, Szell M, Santi P, Díaz-Guilera A, Ratti C (2015) Supersampling and Network Reconstruction of Urban Mobility. PLoS ONE 10(8): e0134508. http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=16c19cca9e&e=55e25a0e3e ;

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ed6ad378e4&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=854c2ed4e5&e=55e25a0e3e)


The collaborative roots of corruption

    Recent financial scandals highlight the devastating consequences of corruption. While much is known about individual immoral behavior, little is known about the collaborative roots of curruption. In a novel experimental paradigm, people could adhere to one of two competing moral norms: collaborate vs. be honest. Whereas collaborative settings may boost honesty due to increased observability, accountability, and reluctance to force others to become accomplices, we show that collaboration, particularly on equal terms, is inductive to the emergence of corruption. When partners' profits are not aligned, or when individuals complete a comparable task alone, corruption levels drop. These findings reveal a dark side of collaboration, suggesting that human cooperative tendencies, and not merely greed, take part in shaping corruption.

The collaborative roots of corruption
Ori Weisela and Shaul Shalvi

PNAS

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0a497d0abc&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=6581c90986&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=889c0337a8&e=55e25a0e3e)



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Editor-in-Chief: Carlos Gershenson.

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