A type-2 fuzzy community detection model in large-scale social networks considering two-layer graphs
Creators: name: Bastani, Susan
name: Naderipour, Mansoureh
name: Zarandi, Mohammad Hossein Fazel
Publisher: name: figshare
Year: 2020
Subjects: subject: 140218 Urban and Regional Economics
subject: FOS: Economics and business
subject: FOS: Economics and business
subject: 140217 Transport Economics
subject: 140216 Tourism Economics
subject: 130108 Technical, Further and Workplace Education
subject: FOS: Educational sciences
subject: FOS: Educational sciences
subject: Sociology
subject: FOS: Sociology
subject: FOS: Sociology
subject: 130106 Secondary Education
subject: 140215 Public Economics- Taxation and Revenue
subject: 140214 Public Economics- Publically Provided Goods
subject: 140213 Public Economics- Public Choice
subject: 130105 Primary Education (excl. Maori)
subject: 140212 Macroeconomics (incl. Monetary and Fiscal Theory)
subject: 140211 Labour Economics
subject: 140210 International Economics and International Finance
subject: 140209 Industry Economics and Industrial Organisation
subject: 140101 History of Economic Thought
subject: 149903 Heterodox Economics
subject: 140208 Health Economics
subject: 140207 Financial Economics
subject: 140206 Experimental Economics
subject: 130199 Education systems not elsewhere classified
subject: Education
subject: 140204 Economics of Education
subject: 149999 Economics not elsewhere classified
subject: Economics
subject: 140199 Economic Theory not elsewhere classified
subject: 140203 Economic History
subject: 140399 Econometrics not elsewhere classified
subject: 149902 Ecological Economics
subject: 140301 Cross-Sectional Analysis
subject: 149901 Comparative Economic Systems
subject: Archaeology
subject: Anthropology

description: This paper mainly aims to identify communities with different interactions between nodes in complex networks. Community detection algorithms partition vertices into denselyconnected components in a complex network. In recent researches, a node is related to multiple aspects of relationships resulting in new challenges in social networks. The two aspects of relationships could be shown as a two-layer graph which comprises two graphs dependent on each other; and each graph shows a specific aspect of the interaction. In this research, a new community detection model is proposed based on the possibilistic cmeans clustering model considering two-layer graphs (PCMTL) in order to detect overlapping communities based on the two-layer graphs using both structural and attribute similarities in large-scale social networks. The nodes are assigned to communities by upper and lower membership values that are indicative of the degree of belonging to the communities through type-2 fuzzy membership values, and the suggested values of interval type-2 fuzzy membership determine how a node belongs to a community with regard to two different aspects of interactions in a two-layer graph. Moreover, according to the proposed model, a validity index is introduced to assess the suggested model in comparison to the approach existing in the literature. Ultimately, two artificial and two real large-scale social networks are used to validate the performance of the suggested model.