Skip to main content

Advertisement

Community structure in networks

© T. Nguyen, B. K. Szymanski, Rensselaer Polytechnic InstituteCommunity structure is one of the most relevant features encountered in numerous real-world applications of networked systems. 

Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few years to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of their structure and dynamics on networked systems. 

The primary goal of this collection is to showcase the cutting edge research advances on community structures in networks, in order to provide a landscape of research progresses and application potentials in related areas.

Lead guest editor
Gergely Palla, Eötvös University, Hungary, pallag@hal.elte.hu

Guest editors 
Hocine Cherifi, Université de Bourgogne, France, hocine.cherifi@u-bourgogne.fr
Boleslaw K. Szymanski, Rensselaer Polytechnic Institute, Troy, USA, szymab@rpi.edu


  1. Content type: Research

    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

    Published on:

  2. Content type: Research

    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

    Published on:

  3. Content type: Research

    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

    Published on:

  4. Content type: Research

    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

    Published on:

  5. Content type: Research

    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

    Published on:

  6. Content type: Research

    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

    Published on:

  7. Content type: Research

    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

    Published on: