Open AccessMarkov Stability Analysis and Community Structure in Social Networks
Author(s) -
Klotilda Nikaj,
Margarita Ifti
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1548/1/012003
Subject(s) - computer science , markov chain , stability (learning theory) , community structure , data science , giant component , complex network , network structure , theoretical computer science , collective behavior , visualization , social network analysis , component (thermodynamics) , data mining , machine learning , random graph , sociology , world wide web , mathematics , social media , graph , combinatorics , anthropology , physics , thermodynamics
Individuals connected to realistic networks exhibit collective behavior. In order to characterize this phenomenon and explore the correlation between collective behaviors and locally interacting elements, we use statistical methods and visualization software as a combined approach to understand the behavior of the network for a given behavior of the agents that we use to recreate our network. The aim of this work is to identify the communities as hierarchical structures trying to find them between a giant component and a small-world network. By analyzing the data and describing how these networks fall in community structure, we aim to obtain new tools and methodology which will help us to describe how networks grow and fall apart in smaller structures, which have similar features with the large network, but different dynamics.