Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.111

A novel social network mining approach for customer segmentation and viral marketing

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Emergence of social networks facilitates individuals to communicate, share opinions and form communities. Organizations benefit from social networks in monitoring customers’ behavior. Social networks mining and analysis aims to segment customers and determine the most influential actors for viral marketing. In this article, we propose a novel social network mining approach for influential analysis and community detection. The community detection task benefits from the most influential users in the network. The proposed approach identifies the most influential users by using a direct mining leaders discovery algorithm and uses these leaders as core points to expand communities around them. This is based on the observation that communities tend to be formed around users of great influence. Extensive experiments have been completed on a real dataset and results show that our approach can contribute in identifying communities of high quality. WIREs Data Mining Knowl Discov 2016, 6:177–189. doi: 10.1002/widm.1183

Proposed architecture for social network mining approach.
[ Normal View | Magnified View ]
Average separability for our proposed algorithm over both similarity measures (weight = common neighbors and common actions).
[ Normal View | Magnified View ]
Average conductance for our proposed algorithm over both similarity measures (weight = common neighbors and common actions).
[ Normal View | Magnified View ]
Average separability for our proposed algorithm versus the hierarchical diffusion algorithm (weight = common actions).
[ Normal View | Magnified View ]
Average conductance for our proposed algorithm versus the hierarchical diffusion algorithm (weight = common actions).
[ Normal View | Magnified View ]
Average separability for our proposed algorithm versus the hierarchical diffusion algorithm (weight = common neighbors).
[ Normal View | Magnified View ]
Average conductance for our proposed algorithm versus the hierarchical diffusion algorithm (weight = common neighbors).
[ Normal View | Magnified View ]
Social network and action log description.
[ Normal View | Magnified View ]
Pseudocode for the proposed community detection algorithm.
[ Normal View | Magnified View ]

Browse by Topic

Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Technologies > Structure Discovery and Clustering

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts