http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Complex Networks & Their Applications IX
Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
1 online resource (XXVIII, 715 pages 216 illustrations, 183 illustrations in color.)
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 Classiﬁcation Using Graph Neural Networks -- Detecting Geographical Competitive Structure for POI Visit Dynamics -- Graph Convolutional Network with Time-based Mini-batch for Information Diﬀusion 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.
edited by Rosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo.
Studies in Computational Intelligence, 944