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  1. Real network datasets provide significant benefits for understanding phenomena such as information diffusion or network evolution. Yet the privacy risks raised from sharing real graph datasets, even when strip...

    Authors: Sameera Horawalavithana, Juan Arroyo Flores, John Skvoretz and Adriana Iamnitchi
    Citation: Applied Network Science 2019 4:33
  2. We develop and test a rewiring method (originally proposed by Newman) which allows to build random networks having pre-assigned degree distribution and two-point correlations. For the case of scale-free degree...

    Authors: Maria Letizia Bertotti and Giovanni Modanese
    Citation: Applied Network Science 2019 4:32
  3. We present a model for network transformation mediated by confinement, as a demonstration of a simple network dynamics that has a direct connection with real world quantities. The model has the capacity of gen...

    Authors: Éder Mílton Schneider, Sebastián Gonçalves, José Roberto Iglesias and Bruno Requião da Cunha
    Citation: Applied Network Science 2019 4:30
  4. Community detection has proved to be extremely successful in a variety of domains. However, most of the algorithms used in practice assume networks are unchanging in time. This assumption is violated for many ...

    Authors: Thomas Magelinski and Kathleen M. Carley
    Citation: Applied Network Science 2019 4:25
  5. Networked power grid systems are susceptible to a phenomenon known as Coherent Swing Instability (CSI), in which a subset of machines in the grid lose synchrony with the rest of the network. We develop network...

    Authors: Daniel Dylewsky, Xiu Yang, Alexandre Tartakovsky and J. Nathan Kutz
    Citation: Applied Network Science 2019 4:24
  6. We study how the community structure of bipartite mutualistic networks changes in a dynamic context. First, we consider a real mutualistic network and introduce extinction events according to several scenarios...

    Authors: Somaye Sheykhali, Juan Fernández-Gracia, Anna Traveset and Víctor M. Eguíluz
    Citation: Applied Network Science 2019 4:23
  7. Transcriptional co-expression networks represent the concerted gene regulation programs by means of statistical inference of co-expression patterns. The rich phenomenology of transcriptional processes behind c...

    Authors: Guillermo de Anda-Jáuregui, Sergio Antonio Alcalá-Corona, Jesús Espinal-Enríquez and Enrique Hernández-Lemus
    Citation: Applied Network Science 2019 4:22
  8. Knowledge graph will be usefull for the intelligent system. As the relationship prediction on the knowledge graph becomes accurate, construction of a knowledge graph and detection of erroneous information incl...

    Authors: Yohei Onuki, Tsuyoshi Murata, Shun Nukui, Seiya Inagi, Xule Qiu, Masao Watanabe and Hiroshi Okamoto
    Citation: Applied Network Science 2019 4:20
  9. A heterogeneous continuous time random walk is an analytical formalism for studying and modeling diffusion processes in heterogeneous structures on microscopic and macroscopic scales. In this paper we study bo...

    Authors: Liubov Tupikina and Denis S. Grebenkov
    Citation: Applied Network Science 2019 4:16
  10. Social networks often has the graph structure of giant strongly connected component (GSCC) and its upstream and downstream portions (IN and OUT), known as a bow-tie structure since a pioneering study on the Wo...

    Authors: Yuji Fujita, Yuichi Kichikawa, Yoshi Fujiwara, Wataru Souma and Hiroshi Iyetomi
    Citation: Applied Network Science 2019 4:15
  11. Growing evidence shows that social media facilitate diffusion of both pro-recovery and anti-recovery information among people affected by mental health problems, while little is known about the associations of...

    Authors: Tao Wang, Markus Brede, Antonella Ianni and Emmanouil Mentzakis
    Citation: Applied Network Science 2019 4:12
  12. Companies face increased product complexity that involves reviewing and optimizing product development business processes.This leads to an increasingly multidisciplinary approach. Research units and multi-loca...

    Authors: Soumaya Yahiaoui, Christophe Courtin, Pierre Maret and Laurent Tabourot
    Citation: Applied Network Science 2019 4:11
  13. As recent work demonstrated, the task of identifying communities in networks can be considered analogous to the classical problem of decoding messages transmitted along a noisy channel. We leverage this analog...

    Authors: Krishna C. Bathina and Filippo Radicchi
    Citation: Applied Network Science 2019 4:9
  14. We examine students’ representations of their conceptions of the interlinked nature of science history and general history, as well as cultural history. Such knowledge landscapes of the history of science are ...

    Authors: Henri Lommi and Ismo T. Koponen
    Citation: Applied Network Science 2019 4:6

    The Correction to this article has been published in Applied Network Science 2020 5:42

  15. This paper studies the driving forces behind the formation of ties within the major communities in the Japanese nationwide network of production, which contains one million firms and five million links between...

    Authors: Hazem Krichene, Abhijit Chakraborty, Yoshi Fujiwara, Hiroyasu Inoue and Masaaki Terai
    Citation: Applied Network Science 2019 4:5
  16. The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attribut...

    Authors: Roberto Interdonato, Martin Atzmueller, Sabrina Gaito, Rushed Kanawati, Christine Largeron and Alessandra Sala
    Citation: Applied Network Science 2019 4:4
  17. In this work, we investigate how court decisions aggregate citations in the European Court of Human Rights. Using the Bass model, we quantify the prevalence of the rich-get-richer phenomenon. We find that the ...

    Authors: Jorge C. Leitão, Sune Lehmann and Henrik Palmer Olsen
    Citation: Applied Network Science 2019 4:3
  18. Electronic healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains und...

    Authors: M. Tarik Altuncu, Erik Mayer, Sophia N. Yaliraki and Mauricio Barahona
    Citation: Applied Network Science 2019 4:2
  19. This paper describes a network reduction technique to reveal possibly hidden relational patterns in information diffusion networks of interlinked content published across different types of online media. Topic...

    Authors: Tobias Hecking, Laura Steinert, Victor H. Masias and H. Ulrich Hoppe
    Citation: Applied Network Science 2019 4:1
  20. Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-...

    Authors: Babak Fotouhi, Naghmeh Momeni, Maria A. Riolo and David L. Buckeridge
    Citation: Applied Network Science 2018 3:46
  21. Understanding the relationship between individuals’ social networks and health could help devise public health interventions for reducing incidence of unhealthy behaviors or increasing prevalence of healthy on...

    Authors: Shikang Liu, David Hachen, Omar Lizardo, Christian Poellabauer, Aaron Striegel and Tijana Milenković
    Citation: Applied Network Science 2018 3:45
  22. Creating a map of actors and their leanings is important for policy makers and stakeholders in the European Commission’s ‘Better Regulation Agenda’. We explore publicly available information about the European...

    Authors: Borut Sluban, Mojca Mikac, Petra Kralj Novak, Stefano Battiston and Igor Mozetič
    Citation: Applied Network Science 2018 3:44
  23. Ideas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand t...

    Authors: Letizia Milli, Giulio Rossetti, Dino Pedreschi and Fosca Giannotti
    Citation: Applied Network Science 2018 3:42
  24. An electroencephalography (EEG) coherence network is a representation of functional brain connectivity, and is constructed by calculating the coherence between pairs of electrode signals as a function of frequ...

    Authors: Chengtao Ji, Natasha M. Maurits and Jos B. T. M. Roerdink
    Citation: Applied Network Science 2018 3:41
  25. In corporate networks, firms are connected through links of corporate ownership and shared directors, connecting the control over major economic actors in our economies in meaningful and consequential ways. Most ...

    Authors: Frank W. Takes, Walter A. Kosters, Boyd Witte and Eelke M. Heemskerk
    Citation: Applied Network Science 2018 3:39
  26. With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learni...

    Authors: John Matta, Junya Zhao, Gunes Ercal and Tayo Obafemi-Ajayi
    Citation: Applied Network Science 2018 3:38
  27. Law enforcement and intelligence agencies worldwide struggle to find effective ways to fight organized crime and reduce criminality. However, illegal networks operate outside the law and much of the data colle...

    Authors: Bruno Requião da Cunha and Sebastián Gonçalves
    Citation: Applied Network Science 2018 3:36

Annual journal metrics

  • Citation Impact 2023
    Journal Impact Factor: 1.3
    5-year Journal Impact Factor: N/A
    Source Normalized Impact per Paper (SNIP): 0.955
    SCImago Journal Rank (SJR): 0.526

    Speed 2023
    Submission to first editorial decision (median days): 11
    Submission to acceptance (median days): 119

    Usage 2023
    Downloads: 581,134
    Altmetric mentions: 776

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