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  1. Understanding how influence is seeded and spreads through social networks is an increasingly important study area. While there are many methods to identify seed nodes that are used to initialize a spread of in...

    Authors: Abida Sadaf, Luke Mathieson, Piotr Bródka and Katarzyna Musial
    Citation: Applied Network Science 2024 9:38
  2. Network visualization is an important tool for extracting information from the structure and configuration of a network, especially when the network includes weighted edges and nodes with attribute information...

    Authors: Emily Chao-Hui Huang, Frederick Kin Hing Phoa and Yan-Hong Chen
    Citation: Applied Network Science 2024 9:37
  3. In this work, we explore the extent to which the spectrum of the graph Laplacian can characterize the probability distribution of random graphs for the purpose of model evaluation and model selection for netwo...

    Authors: Jairo Iván Peña Hidalgo and Jonathan R. Stewart
    Citation: Applied Network Science 2024 9:36
  4. Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of l...

    Authors: Dino Pitoski, Ana Meštrović and Hans Schmeets
    Citation: Applied Network Science 2024 9:35
  5. Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may repre...

    Authors: Leonardo N. Ferreira
    Citation: Applied Network Science 2024 9:32
  6. With real-world network systems typically comprising a large number of interactive components and inherently dynamic, Graph Continual Learning (GCL) has gained increasing popularity in recent years. Furthermor...

    Authors: Liliana Martirano, Dino Ienco, Roberto Interdonato and Andrea Tagarelli
    Citation: Applied Network Science 2024 9:30
  7. Networks provide an understandable and, in the case of small size, visualizable representation of data, which allows us to obtain essential information about the relationships between pairs of nodes, e.g., the...

    Authors: Emanuel Dopater, Eliska Ochodkova and Milos Kudelka
    Citation: Applied Network Science 2024 9:28
  8. Understanding the spread of SARS-CoV-2 has been one of the most pressing problems of the recent past. Network models present a potent approach to studying such spreading phenomena because of their ability to r...

    Authors: Christine Hedde-von Westernhagen, Ayoub Bagheri and Javier Garcia-Bernardo
    Citation: Applied Network Science 2024 9:27
  9. We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primari...

    Authors: Bonan Yang, Mahdi Gharebhaygloo, Hannah Rachel Rondi, Efrosini Hortis, Emilia Zeledon Lostalo, Xiaolan Huang and Gunes Ercal
    Citation: Applied Network Science 2024 9:25
  10. When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrela...

    Authors: Guanqing Chen and A. James O’Malley
    Citation: Applied Network Science 2024 9:24
  11. This study examines sports and physical activities among Chinese aged 18–65, using network analysis on a significant random sample. It categorizes sports into 11 groups based on public selection, with a commun...

    Authors: Xiangyang Bi, Zhanning Sun and Boran Hu
    Citation: Applied Network Science 2024 9:22
  12. In this work, we investigate the analysis of generators for dynamic graphs, which are defined as graphs whose topology changes over time. We focus on generated graphs whose order (number of nodes) varies over ...

    Authors: Vincent Bridonneau, Frédéric Guinand and Yoann Pigné
    Citation: Applied Network Science 2024 9:21
  13. The social networks that interconnect groups of people are often “multi-layered”—comprised of a variety of relationships and interaction types. Although researchers increasingly acknowledge the presence of mul...

    Authors: Aaron Thomas Clark, Jennifer M. Larson and Janet I. Lewis
    Citation: Applied Network Science 2024 9:18
  14. Influence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has attracted attention on tempora...

    Authors: Eric Yanchenko, Tsuyoshi Murata and Petter Holme
    Citation: Applied Network Science 2024 9:16
  15. Reconstructing dynamics of complex systems from sparse, incomplete time series data is a challenging problem with applications in various domains. Here, we develop an iterative heuristic method to infer the un...

    Authors: Zhongqi Cai, Enrico Gerding and Markus Brede
    Citation: Applied Network Science 2024 9:13
  16. In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptu...

    Authors: Sylvain Fontaine, Floriana Gargiulo, Michel Dubois and Paola Tubaro
    Citation: Applied Network Science 2024 9:8
  17. Inferring relations from correlational data allows researchers across the sciences to uncover complex connections between variables for insights into the underlying mechanisms. The researchers often represent ...

    Authors: Magnus Neuman, Joaquín Calatayud, Viktor Tasselius and Martin Rosvall
    Citation: Applied Network Science 2024 9:6
  18. Pairwise temporal interactions between entities can be represented as temporal networks, which code the propagation of processes such as epidemic spreading or information cascades, evolving on top of them. The...

    Authors: Rémi Vaudaine, Pierre Borgnat, Paulo Gonçalves, Rémi Gribonval and Márton Karsai
    Citation: Applied Network Science 2024 9:3
  19. A growing number of social media studies in the U.S. rely on the characterization of the opinion of individual users, for example, as Democrat- or Republican-leaning, or in continuous scales ranging from most ...

    Authors: Pedro Ramaciotti, Duncan Cassells, Zografoula Vagena, Jean-Philippe Cointet and Michael Bailey
    Citation: Applied Network Science 2024 9:2
  20. Random graphs are statistical models that have many applications, ranging from neuroscience to social network analysis. Of particular interest in some applications is the problem of testing two random graphs f...

    Authors: Anton A. Alyakin, Joshua Agterberg, Hayden S. Helm and Carey E. Priebe
    Citation: Applied Network Science 2024 9:1
  21. The growing data size poses challenges for storage and computational processing time in semi-supervised models, making their practical application difficult; researchers have explored the use of reduced networ...

    Authors: Paulo Eduardo Althoff, Alan Demétrius Baria Valejo and Thiago de Paulo Faleiros
    Citation: Applied Network Science 2023 8:82
  22. In recent years, social networks have become popular among Internet users, and various studies have been performed on the analysis of users’ behavior in social networks. Information diffusion analysis is one o...

    Authors: Yoosof Mashayekhi, Alireza Rezvanian and S. Mehdi Vahidipour
    Citation: Applied Network Science 2023 8:81
  23. The degree of polarization in many societies has become a pressing concern in media studies. Typically, it is argued that the internet and social media have created more media producers than ever before, allow...

    Authors: Nicholas Rabb, Lenore Cowen and Jan P. de Ruiter
    Citation: Applied Network Science 2023 8:78
  24. Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position ...

    Authors: Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park and Carey E. Priebe
    Citation: Applied Network Science 2023 8:75
  25. The fundamental objective of the Lightning Network is to establish a decentralized platform for scaling the Bitcoin network and facilitating high-throughput micropayments. However, this network has gradually d...

    Authors: Mohammad Saleh Mahdizadeh, Behnam Bahrak and Mohammad Sayad Haghighi
    Citation: Applied Network Science 2023 8:73

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

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    Downloads: 581,134
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