Network Science

7th International Winter Conference, NetSci-X 2022, Porto, Portugal, February 8¿11, 2022, Proceedings
Herausgegeben:Ribeiro, Pedro; Silva, Fernando; Mendes, José Fernando; Laureano, Rosário

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed proceedings of the 7th International Conference and School of Network Science, NetSci-X 2022, held in Porto, Portugal, in February 2021.

The 13 full papers were carefully reviewed and selected from 19 submissions. The papers deal with the study of network models in domains ranging from biology and physics to computer science, from financial markets to cultural integration, and from social media to infectious diseases.

Produktdetails

• Lecture Notes in Computer Science 13197
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-97239-4
• 1st ed. 2022
• Seitenzahl: 200
• Erscheinungstermin: 25. Februar 2022
• Englisch
• Abmessung: 235mm x 155mm x 12mm
• Gewicht: 312g
• ISBN-13: 9783030972394
• ISBN-10: 3030972399
• Artikelnr.: 63340183

Inhaltsangabe

Using localized attacks with probabilistic failures to model seismic events over physical-logical interdependent network.- A Historical Perspective On International Treaties Via Hypernetwork Science.- On the Number of Edges of the Frechet Mean and Median Graphs.- Core but not Peripheral Online Social Ties is a Protective Factor against Depression: Evidence from a Nationally Representative Sample of Young Adults.- Deep Topological Embedding with Convolutional Neural Networks for Complex Network Classification.- Modularity-based Backbone Extraction in Weighted Complex Networks.- Vessel destination prediction using a graph-based machine learning model.- Hunting for Dual-target Set on a Class of Hierarchical Networks.- Generalized Linear Models Network Autoregression .- Constructing Provably Robust Scale-free Networks.- Functional characterization of transcriptional regulatory networks of yeast species.- Competitive Information Spreading on Modular Networks.- HyperNetVec: Fast and Scalable Hierarchical Embedding for Hypergraphs.