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Titlebook: Complex Networks VII; Proceedings of the 7 Hocine Cherifi,Bruno Gonçalves,Roberta Sinatra Conference proceedings 2016 Springer Internationa

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书目名称Complex Networks VII
副标题Proceedings of the 7
编辑Hocine Cherifi,Bruno Gonçalves,Roberta Sinatra
视频videohttp://file.papertrans.cn/232/231496/231496.mp4
概述Recent research on Complex Networks.Results of the 7th Workshop on Complex Networks CompleNet held 23-25.March 2016 in Dijon, France.Focuses on the interdisciplinary nature of complex-network approach
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Complex Networks VII; Proceedings of the 7 Hocine Cherifi,Bruno Gonçalves,Roberta Sinatra Conference proceedings 2016 Springer Internationa
描述.The lastdecades have seen the emergence of Complex Networks as the language with whicha wide range of complex phenomena in fields as diverse as Physics, ComputerScience, and Medicine (to name just a few) can be properly described andunderstood. This book provides a view of the state of the art in this dynamicfield and covers topics ranging from network controllability, social structure,online behavior, recommendation systems, and network structure. This book includes the peer-reviewed list ofworks presented at the 7.th. Workshop on Complex Networks CompleNet2016 which was hosted by the .Université de Bourgogne, France., from March23-25, 2016. The 28 carefully reviewed and selectedcontributions in this book address many topics related to complex networks andhave been organized in seven major groups: (1) Theory of Complex Networks, (2)Multilayer networks, (3) Controllability of networks, (4) Algorithms fornetworks, (5) Community detection, (6) Dynamics and spreading phenomena onnetworks, (7) Applications of Networks..
出版日期Conference proceedings 2016
关键词Computational Intelligence; Complex Networks; COMPLENET; Network Science; Networks; complexity
版次1
doihttps://doi.org/10.1007/978-3-319-30569-1
isbn_softcover978-3-319-80839-0
isbn_ebook978-3-319-30569-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer International Publishing Switzerland 2016
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https://doi.org/10.1007/978-3-031-44274-2orithm to infer nodes and edges in an unknown network. Our algorithm utilizes monitors that detect incident edges and adjacent nodes with their labels and degrees. The algorithm infers the network through a preferential random walk with a probabilistic restart at a previously discovered but unmonito
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Ezra N. Hoch,Danny Bickson,Danny Dolevre, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating th
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