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Kategorie szczegółowe BISAC

Community Structure of Complex Networks

ISBN-13: 9783642318207 / Angielski / Twarda / 2013 / 117 str.

Hua-Wei Shen
Community Structure of Complex Networks Hua-Wei Shen 9783642318207 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Community Structure of Complex Networks

ISBN-13: 9783642318207 / Angielski / Twarda / 2013 / 117 str.

Hua-Wei Shen
cena 402,53
(netto: 383,36 VAT:  5%)

Najniższa cena z 30 dni: 385,52
Termin realizacji zamówienia:
ok. 22 dni roboczych.

Darmowa dostawa!
inne wydania

Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of overlapping and hierarchical community structures, the detection of multi-scale communities in heterogeneous networks, and the exploration of multiple types of structural regularities. These advances have been successfully applied to analyze large-scale online social networks, such as Facebook and Twitter. This book provides readers a convenient way to grasp the cutting edge of community detection in complex networks.
The thesis on which this book is based was honored with the "Top 100 Excellent Doctoral Dissertations Award" from the Chinese Academy of Sciences and was nominated as the "Outstanding Doctoral Dissertation" by the Chinese Computer Federation.

Kategorie:
Informatyka, Internet
Kategorie BISAC:
Computers > Data Science - Data Analytics
Mathematics > Prawdopodobieństwo i statystyka
Wydawca:
Springer
Seria wydawnicza:
Springer Theses
Język:
Angielski
ISBN-13:
9783642318207
Rok wydania:
2013
Wydanie:
2013
Ilość stron:
117
Waga:
0.37 kg
Wymiary:
24.1 x 16.2 x 1.6
Oprawa:
Twarda
Wolumenów:
01

From the book reviews:

"The topic of this book is the analysis of community structures. ... this book provides a unique viewpoint on network analysis. It is a good handbook for engineers specializing in modern network analysis." (Hsun-Hsien Chang, Computing Reviews, June, 2014)


"The monograph offers an exceptional set of methods of research on networks, and can be useful and interesting to researchers and students in various areas." (Stan Lipovetsky, Technometrics, Vol. 30 (1), 2018)

Community structure: An Introduction.- Detecting the overlapping and hierarchical community structure in networks.- Multiscale community detection in networks with heterogeneous degree distributions.- Community structure and diffusion dynamics on networks.- Exploratory Analysis of the structural regularities in networks.

Hua-Wei Shen is currently an associate professor at the Institute of Computing Technology, Chinese Academy of Sciences, where he leads a research group on network analysis and social computing. His main research interests include network science, recommender system, and social network analysis. He received his PhD from the Graduate University of the Chinese Academy of Sciences in 2010. His doctoral thesis was honored with the “Top 100 Excellent Doctoral Dissertations Award” by the Chinese Academy of Sciences and was nominated as the “Outstanding Doctoral Dissertation” by the Chinese Computer Federation. He has published more than 40 papers in prestigious journals and top international conferences, including PLoS ONE, Physical Review E, Journal of Statistical Mechanics, WWW, CIKM, WSDM, and IJCAI.

Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of overlapping and hierarchical community structures, the detection of multi-scale communities in heterogeneous networks, and the exploration of multiple types of structural regularities. These advances have been successfully applied to analyze large-scale online social networks, such as Facebook and Twitter. This book provides readers a convenient way to grasp the cutting edge of community detection in complex networks.
The thesis on which this book is based was honored with the “Top 100 Excellent Doctoral Dissertations Award” from the Chinese Academy of Sciences and was nominated as the “Outstanding Doctoral Dissertation” by the Chinese Computer Federation.



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