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Community structure in networks

  1. Markov clustering is an effective unsupervised pattern recognition algorithm for data clustering in high-dimensional feature space. However, its community detection performance in complex networks has been dem...

    Authors: Claudio Durán, Alessandro Muscoloni and Carlo Vittorio Cannistraci
    Citation: Applied Network Science 2021 6:29
  2. We provide a novel family of generative block-models for random graphs that naturally incorporates degree distributions: the block-constrained configuration model. Block-constrained configuration models build ...

    Authors: Giona Casiraghi
    Citation: Applied Network Science 2019 4:123
  3. The existence of groups of nodes with common characteristics and the relationships between these groups are important factors influencing the structures of social, technological, biological, and other networks...

    Authors: Milos Kudelka, Eliska Ochodkova, Sarka Zehnalova and Jakub Plesnik
    Citation: Applied Network Science 2019 4:81
  4. This paper examines the process of protest claim-making by reconstructing the semantic structure of online communication that took place prior to the first street event of a protest. Topic networks are identif...

    Authors: Eunkyung Song
    Citation: Applied Network Science 2019 4:60
  5. The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circum...

    Authors: Natalie Stanley, Thomas Bonacci, Roland Kwitt, Marc Niethammer and Peter J. Mucha
    Citation: Applied Network Science 2019 4:54
  6. 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
  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. 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
  9. 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
  10. 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