Complex Networks & Their Applications IX

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.

Presents the latest research on complex networks and their applications Gathers the edited proceedings of the Ninth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020), which was held in Madrid, Spain, on December 13, 2020 Offers state-of-the-art research on complex networks, including various research directions

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