By Adam Wierzbicki, Ulrik Brandes, Frank Schweitzer, Dino Pedreschi
This booklet constitutes the refereed court cases of the twelfth overseas convention and college 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 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.
Read or Download Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings PDF
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Extra resources for Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings
Secondly, Post-Popularity performs much better than Posted-Content. One possible reason is that inferring topics from tweets is still a challenging problem as tweets are short and people use many informal and idiosyncratic words in tweets . The performance of Post-Popularity shows that there are some “universal” posting topics such as “TV & Films” and “Music”. Finally, the combined ranking method achieves the best performance. Reading Topic Discovery. We use all the survey participants in reading topic discovery evaluation.
Next, we map L-topics in X to topics in Y . , γyt . Each L-topic xk ∈ X is represented as a word distribution. , γxk . , ytk = arg maxyt |γxk ∩γyt |. In this way, we can map every L-topic xk to a topic ytk . It is possible to have multiple L-topics mapped to one topic in Y . Finally, with the mapping from X to Y , we determine user topic distribution as follows. , l1 , l2 , . . , lK where lk represents how likely the user is interested in xk . , zt = tk =t lk . Thus we obtain a topic distribution z1 , z2 , .
The company operates ∼ 120 shops in the West Coast in Italy. It serves millions of customers every year, of which a large majority is identiﬁable through ﬁdelity cards. We analyze all items sold from January 2007 to June 2014. We connect each customer to all items she purchased during the observation period, reconstructing 30 quarterly bipartite customer-product networks. For each network, we quantify the average sophistication of the customers and we test its correlation with GDP, for diﬀerent temporal shift values.