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El. knyga: Complex Networks & Their Applications IX: Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020

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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 2020Didesnis paveikslėlis
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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.

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