Title

CNL: Collective Network Linkage across Heterogeneous Social Platforms

Publication Type

Conference Proceeding Article

Publication Date

11-2015

Abstract

The popularity of social media has led many users to create accounts with different online social networks. Identifying these multiple accounts belonging to same user is of critical importance to user profiling, community detection, user behavior understanding and product recommendation. Nevertheless, linking users across heterogeneous social networks is challenging due to large network sizes, heterogeneous user attributes and behaviors in different networks, and noises in user generated data. In this paper, we propose an unsupervised method, Collective Network Linkage (CNL), to link users across heterogeneous social networks. CNL incorporates heterogeneous attributes and social features unique to social network users, handles missing data, and performs in a collective manner. CNL is highly accurate and efficient even without training data. We evaluate CNL on linking users across different social networks. Our experiment results on a Twitter network and another Foursquare network demonstrate that CNL performs very well and its accuracy is superior than the supervised Mobius approach.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

IEEE International Conference on Data Mining ICDM 2015: November 14-17, 2015, Atlantic City: Proceedings

First Page

757

Last Page

762

ISBN

9781467395038

Identifier

10.1109/ICDM.2015.34

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/ICDM.2015.34

This document is currently not available here.

Share

COinS