Download Complex Networks & Their Applications V: Proceedings of by Hocine Cherifi, Sabrina Gaito, Walter Quattrociocchi, PDF

By Hocine Cherifi, Sabrina Gaito, Walter Quattrociocchi, Alessandra Sala

This e-book highlights state-of-the-art study within the box of community technology, supplying scientists, researchers and graduate scholars a distinct chance to make amends for the most recent advances in thought and a large number of functions. It provides the peer-reviewed complaints of the 5th foreign Workshop on advanced Networks & their functions (COMPLEX NETWORKS 2016), which came about in Milan over the last week of November 2016. The rigorously chosen papers are divided into eleven sections reflecting the range and richness of analysis parts within the box. extra particularly, the subsequent themes are lined: community versions; community measures; group constitution; community dynamics; Diffusion, epidemics and spreading techniques; 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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Extra info for Complex Networks & Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016)

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In the case of CL, BTER and RMAT, this is probably due to a large number of degree-0 nodes. The original network consists of a giant component and 3 small components; ReCoN scales them linearly, which is due to its parametrization. The original network is split into eight non-trivial communities, that number should increase slowly according to Sec. 2. Only in the networks generated by BTER, ReCoN and LFR, PLM can find a significant and increasing amount of communities. While PLM finds over 100 non-trivial communities in the network generated by BTER, there are fewer communities detectable in the networks generated by ReCoN and even less in the ones generated by LFR.

Surprisingly, the maximum degree strongly increases up to 10 or 15 thousand with HUDG and BA generators, respectively. The original maximum degree is 248, so that the new value is even significantly higher than the scaled maximum degree (i. e. 248 · 32). Actually, from the scaling study in Sec. 2, we could expect an increase, but rather in a lower range, so the degree distribution of BA and HUDG generators are not realistic. Concerning the Gini coefficient, one can clearly see that ER does not generate a skewed degree distribution at all.

ACM (2010) Generating Scaled Replicas of Real-World Complex Networks Christian L. Staudt, Michael Hamann, Ilya Safro, Alexander Gutfraind and Henning Meyerhenke Abstract Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks can be generated by formal rules. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks.

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