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Special Issue of the 11th International Conference on Complex Networks and their Applications

Since 2012, the International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS) brings together researchers from different scientific communities working on areas related to network science in order to cross-fertilize ideas among scientists. The contributions selected for submission to this special issue reflect the latest problems, advances, and diversity within the network science community.

Submission

The submission article must be original, unpublished and not currently reviewed by other journals. Authors must mention in their cover letter for each SI manuscript that the particular manuscript is for the theme and name of Guest Editors of SI consideration so that the Guest Editors can be notified separately. Please visit https://submission.nature.com/new-submission/41109/3  

Please select the appropriate Collection title “Special Issue of the 11th International Conference on Complex Networks and their Applications” under the “Details” tab during the submission stage.

Lead Guest Editor

Sabrina Gaito, Università degli, Studi di Milano, Italy 
sabrina.gaito@unimi.it 

 
Guest Editors

Giacomo Fiumara, Università degli Studi di Messina, Italy
giacomo.fiumara@unime.it       

Roberto Interdonato, CIRAD, France       
roberto.interdonato@cirad.fr 

Salvatore Miccichè, Università degli Studi di Palermo, Italy               
salvatore.micciche@unipa.it  

Michele Tumminello, Università degli Studi di Palermo, Italy   
michele.tumminello@unipa.it 

Matteo Zignani, Università degli Studi di Milano, Italy
matteo.zignani@gmail.com        

For more information please contact the editors. 



  1. Role discovery is the task of dividing the set of nodes on a graph into classes of structurally similar roles. Modern strategies for role discovery typically rely on graph embedding techniques, which are capab...

    Authors: Eoghan Cunningham and Derek Greene
    Citation: Applied Network Science 2023 8:28
  2. The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with simi...

    Authors: Bogumił Kamiński, Paweł Prałat and François Théberge
    Citation: Applied Network Science 2023 8:25
  3. Empirical studies of the spread of something through social networks, a process often called diffusion, tend to rely on network data assembled from the measurement of multiple kinds of social ties. These can b...

    Authors: Jennifer M. Larson and Pedro L. Rodriguez
    Citation: Applied Network Science 2023 8:21

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