Buch, Englisch, 122 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 395 g
Proceedings of the 13th Conference on Complex Networks, CompleNet 2022
Buch, Englisch, 122 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 395 g
Reihe: Springer Proceedings in Complexity
ISBN: 978-3-031-17657-9
Verlag: Springer International Publishing
This book contains contributions presented at the 13th International Conference on Complex Networks (CompleNet), April 19–22, 2022. CompleNet is an international conference on complex networks that brings together researchers and practitioners from diverse disciplines—from sociology, biology, physics, and computer science—who share a passion to better understand the interdependencies within and across systems. CompleNet is a venue to discuss ideas and findings about all types of networks, from biological to technological and to informational and social. It is this interdisciplinary nature of complex networks that CompleNet aims to explore and celebrate.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
- Mathematik | Informatik Mathematik Operations Research Graphentheorie
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein
Weitere Infos & Material
Targeted Attacks on the World Air Transportation Network: Impact on its Regional Structure.- Measuring Equality and Hierarchical Mobility on Abstract Complex Networks.- Broadcast Graphs with Nodes of Limited Memory.- Investigating the Origins of Fractality Based on Two Novel Fractal Network Models.- A Data-Driven Approach to Cattle Epidemic Modelling under Uncertainty.- Building a Reliable, Dynamic and Temporal Synthetic Model of the World Trade Web.- Co-attention based Multi-contextual Fake News Detection.- Correlation Financial Networks of an Unstable Stock Market: Empirical Study.- Extracting Characteristic Areas Based on Topic Distribution over Proximity Tree.
