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Complex Networks & Their Applications IX

Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
BuchGebunden
715 Seiten
Englisch
Springererschienen am05.01.20211st ed. 2021
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications.mehr
Verfügbare Formate
BuchGebunden
EUR320,99
BuchKartoniert, Paperback
EUR320,99
E-BookPDF1 - PDF WatermarkE-Book
EUR309,23

Produkt

KlappentextThis book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications.
Details
ISBN/GTIN978-3-030-65350-7
ProduktartBuch
EinbandartGebunden
Verlag
Erscheinungsjahr2021
Erscheinungsdatum05.01.2021
Auflage1st ed. 2021
ReiheStudies in Computational Intelligence
Seiten715 Seiten
SpracheEnglisch
IllustrationenXXVIII, 715 p. 216 illus., 183 illus. in color.
Artikel-Nr.49084522

Inhalt/Kritik

Inhaltsverzeichnis
Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales.- On the Impact of Communities on Semi-supervised Classiï¬cation Using Graph Neural Networks.- Detecting Geographical Competitive Structure for POI Visit Dynamics.- Graph Convolutional Network with Time-based Mini-batch for Information Diï¬usion Prediction.- Experimental Evaluation of Train and Test Split Strategies in Link Prediction.- Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings.- Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles.- Topological Analysis of Synthetic Models for Air Transportation Multilayer Networks.- Self-Modeling Networks Using Adaptive Internal Mental Models for Cognitive Analysis and Support Processes.- Extending DeGroot Opinion Formation for Signed Graphs and Minimising Polarization.- Applying Fairness Constraints on Graph Node Ranks Under Personalization Bias.mehr

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