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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The submission article must be original, unpublished and not currently reviewed by other journals.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. Network science offers valuable tools for planning and managing public transportation systems, with measures such as network centralities proposed as complementary predictors of ridership. This paper explores ...

    Authors: Athanasios Kopsidas, Aristeides Douvaras and Konstantinos Kepaptsoglou
    Citation: Applied Network Science 2023 8:69
  2. Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an e...

    Authors: Naw Safrin Sattar, Aydin Buluc, Khaled Z. Ibrahim and Shaikh Arifuzzaman
    Citation: Applied Network Science 2023 8:64
  3. This paper empirically investigates the role played by cross-country spillovers in shaping spatiotemporal differences in country income. While existing literature focused on effects captured by direct spillove...

    Authors: Giorgio Fagiolo and Davide Samuele Luzzati
    Citation: Applied Network Science 2023 8:59
  4. Hospital databases provide complex data on individual patients, which can be analysed to discover patterns and relationships. This can provide insight into medicine that cannot be gained through focused studie...

    Authors: Ondrej Janca, Eliska Ochodkova, Eva Kriegova, Pavel Horak, Martina Skacelova and Milos Kudelka
    Citation: Applied Network Science 2023 8:57
  5. The present study aims to infer individuals’ social networks from their spatio-temporal behavior acquired via wearable sensors. Previously proposed static network metrics (e.g., centrality measures) cannot cap...

    Authors: Maedeh Nasri, Mitra Baratchi, Yung-Ting Tsou, Sarah Giest, Alexander Koutamanis and Carolien Rieffe
    Citation: Applied Network Science 2023 8:50
  6. Aiming at knowledge discovery for temporal sequences of cooking recipes published in social media platforms from the viewpoint of network science, we consider an analysis of temporal higher-order networks of i...

    Authors: Koudai Fujisawa, Masahito Kumano and Masahiro Kimura
    Citation: Applied Network Science 2023 8:48
  7. Social media platforms centered around content creators (CCs) faced rapid growth in the past decade. Currently, millions of CCs make livable incomes through platforms such as YouTube, TikTok, and Instagram. As...

    Authors: Stefania Ionescu, Anikó Hannák and Nicolò Pagan
    Citation: Applied Network Science 2023 8:46
  8. We discuss the added value of various approaches for identifying similarities in social network communities based on the content they produce. We show the limitations of observing communities using topology-on...

    Authors: Bojan Evkoski, Petra Kralj Novak and Nikola Ljubešić
    Citation: Applied Network Science 2023 8:40

    The Correction to this article has been published in Applied Network Science 2023 8:47

  9. We investigate the statistical learning of nodal attribute functionals in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models,...

    Authors: Nelson Antunes, Sayan Banerjee, Shankar Bhamidi and Vladas Pipiras
    Citation: Applied Network Science 2023 8:39
  10. In the Bitcoin protocol, dust refers to small amounts of currency that are lower than the fee required to spend them in a transaction. Although “economically irrational”, dust is commonly used for achieving un...

    Authors: Matteo Loporchio, Anna Bernasconi, Damiano Di Francesco Maesa and Laura Ricci
    Citation: Applied Network Science 2023 8:34
  11. The widely used characterization of scale-free networks as “robust-yet-fragile” originates primarily from experiments on instances generated by preferential attachment. According to this characterization, scal...

    Authors: Rouzbeh Hasheminezhad and Ulrik Brandes
    Citation: Applied Network Science 2023 8:32
  12. 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
  13. 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
  14. 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

Annual journal metrics

  • 2022 Citation Impact
    2.2 - 2-year Impact Factor
    1.117 - SNIP (Source Normalized Impact per Paper)
    0.603 - SJR (SCImago Journal Rank)

    2022 Speed
    12 days submission to first editorial decision for all manuscripts (Median)
    137 days submission to accept (Median)

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