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selected as I mellow out at Lake Como Bellagio

   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
                  Streisand/Sondheim
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   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
   http://www.chass.utoronto.ca/~wellman            http://amzn.to/zXZg39
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Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0f7204b1d3&e=55e25a0e3e

A physical model for efficient ranking in networks

    We present a principled model and algorithm to infer a hierarchical ranking of nodes in directed networks. Unlike other methods such as minimum violation ranking, it assigns real-valued scores to nodes rather than simply ordinal ranks, and it formalizes the assumption that interactions are more likely to occur between individuals with similar ranks. It provides a natural framework for a statistical significance test for distinguishing when the inferred hierarchy is due to the network topology or is instead due to random chance, and it can be used to perform inference tasks such as predicting the existence or direction of edges. The ranking is inferred by solving a linear system of equations, which is sparse if the network is; thus the resulting algorithm is extremely efficient and scalable. We illustrate these findings by analyzing real and synthetic data and show that our method outperforms others, in both speed and accuracy, in recovering the underlying ranks and predicting
edge directions.


A physical model for efficient ranking in networks
Caterina De Bacco, Daniel B. Larremore, Cristopher Moore

Source: arxiv.org (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8af69de538&e=55e25a0e3e)



Relatedness, Knowledge Diffusion, and the Evolution of Bilateral Trade

    During the last decades two important contributions have reshaped our 
understanding of international trade. First, countries trade more with 
those with whom they share history, language, and culture, suggesting that 
trade is limited by information frictions. Second, countries are more 
likely to start exporting products that are similar to their current 
exports, suggesting that knowledge diffusion among related industries is a 
key constrain shaping the diversification of exports. But does knowledge 
about how to export to a destination also diffuses among related products 
and geographic neighbors? Do countries need to learn how to trade each 
product to each destination? Here, we use bilateral trade data from 2000 
to 2015 to show that countries are more likely to increase their exports 
of a product to a destination when: (i) they export related products to 
it, (ii) they export the same product to the neighbor of a destination, 
(iii) they have neighbors who export the same product to that destination. 
Then, we explore the magnitude of these effects for new, nascent, and 
experienced exporters, (exporters with and without comparative advantage 
in a product) and also for groups of products with different level of 
technological sophistication. We find that the effects of product and 
geographic relatedness are stronger for new exporters, and also, that the 
effect of product relatedness is stronger for more technologically 
sophisticated products. These findings support the idea that international 
trade is shaped by information frictions that are reduced in the presence 
of related products and experienced geographic neighbors.


Relatedness, Knowledge Diffusion, and the Evolution of Bilateral Trade
Bogang Jun, Aamena Alshamsi, Jian Gao, Cesar A Hidalgo

Source: arxiv.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=dd982a303d&e=55e25a0e3e)



Evidence of complex contagion of information in social media: An experiment using Twitter bots

    It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using   social bots   deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple
and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.


M°nsted B, Sapie  y  ski P, Ferrara E, Lehmann S (2017) Evidence of complex contagion of information in social media: An experiment using Twitter bots. PLoS ONE 12(9): e0184148. http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=145e50ab4c&e=55e25a0e3e

Source: journals.plos.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d497c430eb&e=55e25a0e3e)

Explorability and the origin of network sparsity in living systems
Daniel M. Busiello, Samir Suweis, Jorge Hidalgo & Amos Maritan
Scientific Reports 7, Article number: 12323 (2017)
doi:10.1038/s41598-017-12521-1

Source: www.nature.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8201dbf9f6&e=55e25a0e3e)


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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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