By Hocine Cherifi, Sabrina Gaito, Walter Quattrociocchi, Alessandra Sala
This publication highlights state-of-the-art study within the box of community technological know-how, supplying scientists, researchers and graduate scholars a different chance to compensate for the latest advances in concept and a large number of purposes. It offers the peer-reviewed complaints of the 5th overseas Workshop on complicated Networks & their functions (COMPLEX NETWORKS 2016), which happened in Milan over the past week of November 2016. The rigorously chosen papers are divided into eleven sections reflecting the variety and richness of analysis parts within the box. extra particularly, the subsequent subject matters are coated: community versions; community measures; neighborhood constitution; community dynamics; Diffusion, epidemics and spreading tactics; Resilience and regulate; community visualization; Social and political networks; Networks in finance and economics; organic and ecological networks; and community analysis.
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Additional info for Complex Networks & Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016)
RMAT. The Recursive Matrix (RMAT) model  was proposed to recreate various properties of complex networks, including an optional power-law degree distribution, the small-world property and self-similarity. The RMAT model can only generate Generating Scaled Replicas of Real-World Complex Networks 23 graphs with 2s nodes, where s is an integer scaling parameter. In order to target a fixed number of nodes nr , we calculate s so that 2s > nr and delete 2s − nr random nodes. The choice of other parameters as well as the running time of fitting are discussed in .
The mixing parameter is set to the ratio between intra- and inter-cluster edges of the found communities. The details are described in . 5 Computational Experiments Our implementations of ReCoN and the various fitting methods are based on NetworKit , a tool suite for scalable network analysis. It also contains many of the generators we use for comparison and provides a large set of graph algorithms we use for our experiments. NetworKit combines C++ kernels with an interactive Python shell to achieve both high performance and interactivity, a concept we use for our implementations as well.
We examine if this is indeed the case with the generative models we consider. Using the previously described generators Fig. 4 Fitting and generating: processing speed measured in edges/s (size of replica graph / total running time, measured on 100 Facebook graphs) C. L. Staudt, M. Hamann, I. Safro, A. Gutfraind, H. Meyerhenke #$* 26 #$) #$( #$' #$& ! #$% " and fitting schemes, we generate replicas of 100 Facebook networks and test a variety of graph algorithms (see Sec.