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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 2021 ed. [Kietas viršelis]

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  • Formatas: Hardback, 715 pages, aukštis x plotis: 235x155 mm, weight: 1274 g, 183 Illustrations, color; 33 Illustrations, black and white; XXVIII, 715 p. 216 illus., 183 illus. in color., 1 Hardback
  • Serija: Studies in Computational Intelligence 944
  • Išleidimo metai: 05-Jan-2021
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030653501
  • ISBN-13: 9783030653507
Kitos knygos pagal šią temą:
Complex Networks & Their Applications IX: Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020 2021 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 715 pages, aukštis x plotis: 235x155 mm, weight: 1274 g, 183 Illustrations, color; 33 Illustrations, black and white; XXVIII, 715 p. 216 illus., 183 illus. in color., 1 Hardback
  • Serija: Studies in Computational Intelligence 944
  • Išleidimo metai: 05-Jan-2021
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030653501
  • ISBN-13: 9783030653507
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783030653507.html
Raktažodžiai:

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. It presents the peer-reviewed proceedings of the IX International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020). The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, network dynamics; diffusion, epidemics and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks and technological networks. 

 

Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales.- On the Impact of Communities on Semi-supervised Classi?cation Using Graph Neural Networks.- Detecting Geographical Competitive Structure for POI Visit Dynamics.- Graph Convolutional Network with Time-based Mini-batch for Information Di?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.