Download Complex Networks VII: Proceedings of the 7th Workshop on by Hocine Cherifi, Bruno Gonçalves, Ronaldo Menezes, Roberta PDF

By Hocine Cherifi, Bruno Gonçalves, Ronaldo Menezes, Roberta Sinatra

The final a long time have visible the emergence of advanced Networks because the language with which quite a lot of advanced phenomena in fields as diversified as Physics, computing device technology, and medication (to identify quite a few) might be thoroughly defined and understood. This e-book offers a view of the cutting-edge during this dynamic box and covers themes starting from community controllability, social constitution, on-line habit, advice structures, and community constitution. This booklet contains the peer-reviewed checklist of works offered on the 7th Workshop on advanced Networks CompleNet 2016 which was once hosted through the Université de Bourgogne, France, from March 23-25, 2016. The 28 rigorously reviewed and chosen contributions during this e-book tackle many themes regarding advanced networks and feature been equipped in seven significant teams: (1) thought 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) functions of Networks.

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Additional resources for Complex Networks VII: Proceedings of the 7th Workshop on Complex Networks CompleNet 2016

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The sentiment polarity of a document is computed from the counts of predefined sentiment terms (positive and negative) in the document. The sentiment terms are from the HarvardIV-4 sentiment dictionary [22]. For a document d, the sentiment polarity s is calculated by the following formula: sd = posd − negd , posd + negd where pos and neg are the numbers of positive and negative dictionary terms found in the document d, respectively. The sentiment polarities of a set of documents can then be aggregated.

4 Network Visualization A network visualization offers a unique way to understand and analyze complex systems by enabling the user to easily inspect and comprehend relations between individual units and their properties [16]. In addition to single layer network visualization [2], also multi-layer visualization is increasingly popular [6, 13]. We have implemented a spatio-temporal visualization of the country co-occurrence network, constructed from the detected major news events, their most relevant content, and the associated sentiment.

In our previous work, we have developed a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated [14]. The method was applied to analyze the contents of financial news in comparison to empirical networks, constructed from other data sources, like geographical proximity, trade volumes, and correlations between financial indicators [19]. The above co-occurrence detection method models well the persistent, ‘everyday’ contents of news.

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