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 many years have noticeable the emergence of complicated Networks because the language with which a variety of complicated phenomena in fields as various as Physics, machine technology, and drugs (to identify quite a few) will be thoroughly defined and understood. This publication presents a view of the cutting-edge during this dynamic box and covers issues starting from community controllability, social constitution, on-line habit, suggestion platforms, and community constitution. This booklet comprises the peer-reviewed checklist of works provided on the 7th Workshop on complicated Networks CompleNet 2016 which was once hosted by means of the Université de Bourgogne, France, from March 23-25, 2016. The 28 conscientiously reviewed and chosen contributions during this ebook deal with many subject matters with regards to advanced networks and feature been equipped in seven significant teams: (1) concept of complicated Networks, (2) Multilayer networks, (3) Controllability of networks, (4) Algorithms for networks, (5) neighborhood detection, (6) Dynamics and spreading phenomena on networks, (7) functions of Networks.

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Parallel Distrib. Comput. 48, 96–129 (1998) 20. : On the bias of BFS (breadth first search). In: 22nd International Teletraffic Congress (ITC), pp. 1–8 (2010) 21. : Orbis: rescaling degree correlations to generate annotated Internet topologies. SIGCOMM Comput. Commun. Rev. 37(4), 325–336 (2007) 22. : On the bias of traceroute sampling: or, power-law degree distributions in regular graphs. In: STOC’05: Proceedings of the 37th Annual ACM Symposium on Theory of Computing, Baltimore, MD, May 2005, pp.

Finally, we show the network visualization implemented in our web portal. We conclude in Sect. 4 with ideas for future work. 2 Methods We describe a multi-stage approach to construct a multi-layer network of major news events. The stages consist of entity recognition (which identify nodes of the network), event detection (which identify links between the nodes and the co-occurrence layer), content identification (the summary layer), and sentiment analysis (the sentiment layer). , which we call entities.

593 50 B. Monnot and J. 1 Edge Weight Correlations with Node Degree We find substantial correlations between edge weights and other network properties. These connections seem to be the main drivers of the behavior we observe regarding sensitivity of network controllability to edge weights. , there is a link pointing from u to v. Similarly, in this situation we call v an out-neighbor of u. The in-degree of a node v ∈ V is the number of in-neighbors of v. The out-degree of a node v ∈ V is the number of out-neighbors of v.

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