Complex Networks VII: Proceedings of the 7th Workshop on by Hocine Cherifi, Bruno Gonçalves, Ronaldo Menezes, Roberta
By Hocine Cherifi, Bruno Gonçalves, Ronaldo Menezes, Roberta Sinatra
The final a long time have noticeable the emergence of advanced Networks because the language with which a variety of complicated phenomena in fields as diversified as Physics, desktop technology, and medication (to identify quite a few) could be competently defined and understood. This publication presents a view of the cutting-edge during this dynamic box and covers subject matters starting from community controllability, social constitution, on-line habit, advice structures, and community constitution. This publication comprises the peer-reviewed checklist of works provided on the 7th Workshop on complicated Networks CompleNet 2016 which used to be hosted via the Université de Bourgogne, France, from March 23-25, 2016. The 28 rigorously reviewed and chosen contributions during this ebook deal with many subject matters regarding complicated networks and feature been geared up in seven significant teams: (1) concept of complicated Networks, (2) Multilayer networks, (3) Controllability of networks, (4) Algorithms for networks, (5) group detection, (6) Dynamics and spreading phenomena on networks, (7) purposes of Networks.
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Additional info for Complex Networks VII: Proceedings of the 7th Workshop on Complex Networks CompleNet 2016
We apply Welch’s t-test  which is robust for skewed distributions . The results are in Table 3. 0001, the null hypothesis can be rejected for all pairs of news populations. We conclude, with high confidence, that the three populations of news have significantly different sentiment means, though these differences are small. Of course, with such large samples one always detects differences. Nevertheless, the results are useful to hint at meaningful differences which are exploited by introducing the neutral zone.
All three sentiment distributions are approximately Gaussian, and very similar. There is a minor positive sentiment bias in all news, while the peak news are slightly negative. The top news at peak days also seem relatively less positive than the all news. However, the top news contain proportionally more extremely positive and extremely negative news articles. The statistics are in Table 2. We test the null hypothesis that a pair of news populations (all, peak, or top) has equal mean sentiment.
In: Proceedings of European Conference on Complex Systems, ECCS-14. Springer (2015) 20. : Monitoring the Twitter sentiment during the Bulgarian elections. In: Proceedings of IEEE International Conference on Data Science and Advanced Analytics. IEEE (2015) 21. : Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in technology foresight. Scientometrics 85(1), 65–79 (2010) 22. : More than words: quantifying language to measure firms’ fundamentals. J. Finan. 63(3), 1437–1467 (2008) 23.