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  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. In their previous work, the authors considered the concept of random spanning tree intersection of complex networks (London and Pluhár, in: Cherifi, Mantegna, Rocha, Cherifi, Micciche (eds) Complex networks an...

    Authors: András London and András Pluhár
    Citation: Applied Network Science 2023 8:72
  14. Industries today are tightly interconnected, necessitating a systematic perspective in understanding the complexity of relations. Employing network science, the literature constructs dense production networks ...

    Authors: Eszter Molnár and Dénes Csala
    Citation: Applied Network Science 2023 8:71
  15. 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
  16. Apart from nodes and links, for many networked systems, we have access to data on paths, i.e., collections of temporally ordered variable-length node sequences that are constrained by the system’s topology. Un...

    Authors: Christoph Gote, Giona Casiraghi, Frank Schweitzer and Ingo Scholtes
    Citation: Applied Network Science 2023 8:68
  17. Social media platforms offer cost-effective digital marketing opportunities to monitor the market, create user communities, and spread positive opinions. They allow companies with fewer budgets, like startups,...

    Authors: Ana Rita Peixoto, Ana de Almeida, Nuno António, Fernando Batista, Ricardo Ribeiro and Elsa Cardoso
    Citation: Applied Network Science 2023 8:66
  18. During the April 2023 Brazil–China summit, the creation of a trade currency supported by the BRICS countries was proposed. Using the United Nations Comtrade database, providing the frame of the world trade net...

    Authors: Célestin Coquidé, José Lages and Dima L. Shepelyansky
    Citation: Applied Network Science 2023 8:65
  19. 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
  20. We investigate the development of cooperative behavior in networks over time. In our controlled laboratory experiment, subjects can cooperate by sending costly messages that contain valuable information for th...

    Authors: Gorm Gruner Jensen, Martin Benedikt Busch, Marco Piovesan and Jan O. Haerter
    Citation: Applied Network Science 2023 8:62
  21. Crypto assets have lately become the chief interest of investors around the world. The excitement around, along with the promise of the nascent technology led to enormous speculation by impulsive investors. De...

    Authors: Hendra Setiawan and Moinak Bhaduri
    Citation: Applied Network Science 2023 8:61
  22. The decision of where to locate the catchment area of an urban network exerts significant influence on the indicator values and in this research this influence is referred to as the placement effect. Placement...

    Authors: Hsiao-Hui Chen and Udo Dietrich
    Citation: Applied Network Science 2023 8:60
  23. 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
  24. 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
  25. It is often of interest to sample vertices from a graph with a bias towards higher-degree vertices. One well-known method, which we call random neighbor or RN, involves taking a vertex at random and exchanging...

    Authors: Yitzchak Novick and Amotz Bar-Noy
    Citation: Applied Network Science 2023 8:56
  26. Rapid urbanization has posed challenges to accessibility to critical services that require in-depth analysis. Complex networks theory has been used to evaluate the evolution of network topologies or the overal...

    Authors: Nazli Yonca Aydin, Emre Yigitbasi, Ylenia Casali and Bert van Wee
    Citation: Applied Network Science 2023 8:55
  27. Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limi...

    Authors: Gergely Ódor, Jana Vuckovic, Miguel-Angel Sanchez Ndoye and Patrick Thiran
    Citation: Applied Network Science 2023 8:53
  28. Self-propagating malware (SPM) is responsible for large financial losses and major data breaches with devastating social impacts that cannot be understated. Well-known campaigns such as WannaCry and Colonial P...

    Authors: Alesia Chernikova, Nicolò Gozzi, Nicola Perra, Simona Boboila, Tina Eliassi-Rad and Alina Oprea
    Citation: Applied Network Science 2023 8:52
  29. Nestedness has been observed in a variety of networks but has been primarily viewed in the context of bipartite networks. Numerous metrics quantify nestedness and some clustering methods identify fully nested ...

    Authors: Imre Gera and András London
    Citation: Applied Network Science 2023 8:51
  30. 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

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