User community discovery from multi‐relational networks

User community discovery from multi‐relational networks

0.00 Avg rating0 Votes
Article ID: iaor20132260
Volume: 54
Issue: 2
Start Page Number: 870
End Page Number: 879
Publication Date: Jan 2013
Journal: Decision Support Systems
Authors: , , ,
Keywords: social networks
Abstract:

Online social network services (SNS) have been experiencing rapid growth in recent years. SNS enable users to identify other users with common interests, exchange their opinions, and establish forums for communication, and so on. Discovering densely connected user communities from social networks has become one of the major challenges, to help understand the structural properties of SNS and improve user‐oriented services such as identification of influential users and automated recommendations. Previous work on community discovery has treated user friendship networks and user‐generated contents separately. We hypothesize that these two types of information can be fruitfully integrated and propose a unified framework for user community discovery in online social networks. This framework combines the author‐topic (AT) model with user friendship network analysis. We empirically show that this approach is capable of discovering interesting user communities using two real‐world datasets.

Reviews

Required fields are marked *. Your email address will not be published.