Title :
Community discovery in a growing model of social networks
Author :
Bhukya, Sreedhar
Author_Institution :
Dept. of Comput. & Inf. Sci., Univ. of Hyderabad, Hyderabad, India
Abstract :
A number of recent studies on social networks are based on characteristics which include assortative mixing, high clustering, short average path lengths, broad degree distributions and the existence of community structure. Here, a model which satisfies all the above characteristics is developed. In addition, this model facilitates interaction between different communities. This model gives very high clustering coefficient by retaining the asymptotically scale-free degree distribution. Here the community structure is raised from a mixture of random attachment and implicit preferential attachment. A model for community discovery also has been discovered where strict community structure has been preserved.
Keywords :
pattern clustering; social networking (online); assortative mixing characteristics; average path length characteristics; broad degree distribution characteristics; community discovery; community structure existence characteristics; high clustering characteristics; preferential attachment; random attachment; scale-free degree distribution; social network model; Collaboration; Communities; Computational modeling; Data models; Equations; Mathematical model; Social network services; community discovery; communiy network; social networks;
Conference_Titel :
Business Applications of Social Network Analysis (BASNA), 2010 IEEE International Workshop on
Conference_Location :
Bangalore
Print_ISBN :
978-1-4244-8999-2
DOI :
10.1109/BASNA.2010.5730299