Download Advances in Network Science: 12th International Conference by Adam Wierzbicki, Ulrik Brandes, Frank Schweitzer, Dino PDF

By Adam Wierzbicki, Ulrik Brandes, Frank Schweitzer, Dino Pedreschi

This booklet constitutes the refereed complaints of the twelfth overseas convention and faculty of community technological know-how, NetSci-X 2016, held in Wroclaw, Poland, in January 2016.

The 12 complete and six brief papers have been rigorously reviewed and chosen from fifty nine submissions. The papers care for the research of community types in domain names starting from biology and physics to desktop technology, from monetary markets to cultural integration, and from social media to infectious diseases.

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Additional resources for Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings

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The sophistication measure is created by recursively correcting the degree of each customer in the network. Customers are sophisticated if they purchase sophisticated products, and products are sophisticated if they are bought by sophisticated customers. Once this recursive correction converges, the aggregated sophistication level of the network is our well-being estimation. The average sophistication of the satisfiable needs of a population is a good test of a country’s prosperity as it addresses the two issues of GDP we discussed.

To select the data we use the inter-quantile range, the measure of spread from the first to the third quantile. In practice, we trim the outliers out of the aggregation and then we compute the average, the InterQuartile Mean, or “IQM”. The IQM is calculated as follows: xIQM 2 = n 3n 4 xi i= n 4 +1 assuming n sorted values. Also note that all the timelines we present have been normalized. All variables take values between zero and one, where zero represents the minimum value observed and one the maximum.

Family”). , “music”). We therefore make use of list names to obtain followees with expertise. First, we crawled the number of lists each followee is member of and the names of the lists. The users who are member of only very few lists are usually not well known and these lists are usually for social purpose. We therefore only included those followees who appear in at least 10 lists. For our survey participants, we obtained 15,395 followees. 8,601 of them are public users. Among the 8,601 followees, 43 percent of them appear in at least 10 lists.

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