Network Science

7th International Winter Conference, NetSci-X 2022, Porto, Portugal, February 8–11, 2022, Proceedings

Häftad, Engelska, 2022

Av Pedro Ribeiro, Fernando Silva, José Fernando Mendes, Rosário Laureano, Jose Fernando Mendes, Rosario Laureano

799 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2022-02-25
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieInformation Systems and Applications, incl. Internet/Web, and HCI
Antal sidor185
FörlagSpringer Nature Switzerland AG
ISBN9783030972394

Tillhör följande kategorier

Nätverk och kommunikation inom Data och it

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.