Exponential random graph models for networks with community structure
Piotr Fronczak , Agata Fronczak , Maksymilian Bujok
AbstractAlthough the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field. © 2013 American Physical Society.
|Journal series||Physical Review E, ISSN 1539-3755|
|ASJC Classification||; ;|
|Score|| = 35.0, 10-06-2020, ArticleFromJournal|
= 35.0, 10-06-2020, ArticleFromJournal
|Publication indicators||= 13; = 9; : 2014 = 1.123; : 2013 = 2.326 (2) - 2013=2.302 (5)|
|Citation count*||4 (2015-07-21)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.