By Adam Wierzbicki, Ulrik Brandes, Frank Schweitzer, Dino Pedreschi
This publication constitutes the refereed lawsuits of the twelfth overseas convention and college of community technology, 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 take care of the learn of community versions in domain names starting from biology and physics to machine 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
Self-censorship on Facebook. In: ICWSM (2013) 6. : The youtube video recommendation system. In: RecSys (2010) 7. : A uniﬁed model for topics, events and users on Twitter. In: EMNLP (2013) 8. : Cognos: crowdsourcing search for topic experts in microblogs. In: SIGIR (2012) 9. : Characterizing silent users in social media communities. In: ICWSM (2015) 10. : Social media and the “spiral of silence”. Pew Research Internet Project (2014). http://www. org/2014/08/26/social-media-and-the-spiral-of-silence/ 11.
Table 1. The most and least sophisticated products in our dataset. 2 1 Cosmetics 2 Underwear for men 3 Furniture 4 Multimedia services 5 Toys ... −5 ... Fresh Cheese −4 Red Meat −3 Spaghetti −2 Bananas −1 Short Pasta Seasonality Both GDP and the behavior of customers in the retail market are aﬀected by seasonality. Diﬀerent periods of the year are associated with diﬀerent economic activities. This is particularly true for Italy in some instances: during the month of August, Italian productive activities come to an almost complete halt, and the country hosts its peak tourist population.
In the Table, n represents Posting Topics = Reading Topics 25 Table 2. Performance (MAP@n) of posting topic discovery. 76 the number of topics that are chosen as the predicted topics. “Like” means that we use the topics that a user likes to post as the ground truth topics, and “Like and Somewhat Like” means that we use the topics that a user rates “Like” or “Somewhat Like” as the ground truth topics. We observe ﬁrstly that all our ranking strategies yield performance significantly better than Random.