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Record Nr. |
UNINA9910300604603321 |
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Titolo |
Network Intelligence Meets User Centered Social Media Networks / / edited by Reda Alhajj, H. Ulrich Hoppe, Tobias Hecking, Piotr Bródka, Przemyslaw Kazienko |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (VI, 247 p. 63 illus., 54 illus. in color.) |
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Collana |
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Lecture Notes in Social Networks, , 2190-5436 |
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Disciplina |
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Soggetti |
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Sociology - Methodology |
Graph theory |
Data mining |
Telemarketing |
Internet marketing |
Sociological Methods |
Graph Theory |
Data Mining and Knowledge Discovery |
Digital Marketing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Data-based centrality measures -- Extracting the Main Path of historic events from Wikipedia -- Simulating trade in economic networks with TrEcSim -- Community Aliveness: Discovering interaction decay patterns in online social communities -- Network Patterns of Direct and Indirect Reciprocity in edX MOOC Forums -- Targeting influential nodes for recovery in bootstrap percolation on hyperbolic networks -- Trump versus Clinton – Twitter communication during the US primaries -- Extended feature-driven graph model for Social Media Networks -- Market basket analysis using minimum spanning trees -- Behavior-based relevance estimation for social networks interaction relations -- Sponge walker: Community detection in large directed social networks using local structures and random walks -- Identifying promising research topics in Computer Science -- Identifying accelerators of |
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information diffusion across social media channels -- Towards anILP approach for learning privacy heuristics from users' regrets -- Strength of nations: A case study on estimating the influence of leading countries using social media analysis -- Incremental learning in dynamic networks for node classification. |
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Sommario/riassunto |
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This edited volume presents advances in modeling and computational analysis techniques related to networks and online communities. It contains the best papers of notable scientists from the 4th European Network Intelligence Conference (ENIC 2017) that have been peer reviewed and expanded into the present format. The aim of this text is to share knowledge and experience as well as to present recent advances in the field. The book is a nice mix of basic research topics such as data-based centrality measures along with intriguing applied topics, for example, interaction decay patterns in online social communities. This book will appeal to students, professors, and researchers working in the fields of data science, computational social science, and social network analysis. . |
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