Innovative Methods and Measures in Overlapping Community Detection.
In the modern Web, it is common for an active person to have several profiles in different online social networks. As new general-purpose and niche social network services arise every year, the problem of social data integration will likely remain actual in the nearest future. Discovering multiple profiles of a single person across different social networks allows to merge all user’s contacts from different social services or compose more complete social graph that is helpful in many social-powered applications. In this paper we propose a new approach for user profile matching based on Conditional Random Fields that extensively combines usage of profile attributes and social linkage. It is extremely suitable for cases when profile data is poor, incomplete or hidden due to privacy settings. Evaluation on Twitter and Facebook sample datasets showed that our solution significantly outperforms common attribute-based approach and is able to find matches that are not discoverable by using only profile information. We also demonstrate the importance of social links for identity resolution task and show that certain profiles can be matched based only on social relationships between online social networks users.Full text of the paper in pdf
Proceedings of the International Workshop on Experimental Economics and Machine Learning (EEML 2012), Brussel, Belgium.