Rules, Photos, and Crowdsourcing for Relationship Type Discovery in Social Networks
Elie Raad, Richard Chbeir, Albert Dipanda
Journal of Multimedia Processing and Technologies, Vol. 2, No. 2, 2011.
DOI: 
Link: www.dline.info/jmpt/fulltext/v2n2/3.pdf
Abstract:
Relationship discovery has found considerable interest recently due to the growing number of social network users and the increasing need to analyze social interactions. With the huge amount of user-generated content, photos, in particular, are gaining popularity in social networks. Setting the focus on photos is of valuable importance for extracting essential social relationshipsâ€™ evidences about users. In this paper, we present a rule-based approach to discover such relationships among users (e.g., colleagues, relatives, friends, etc.). We propose to automatically generate the set of basic rules through a crowdsourcing methodology using photos. The creation of a knowledge base (i.e. a photo dataset), founded on the wisdom of a large number of web users, is the primary objective that enables the extraction of a set of basic rules. Extending the set of basic rules is another important and essential objective for relationship discovery. Within this context, we provide a set of manually generated common sense rules that are relationship- type independent. In addition, we use data mining to extend the basic rules in order to obtain another set called the derived rules. These derived rules take into account the context of each user and are interestingly useful to identify relationship types yet unlabeled. Experiments results demonstrate the correctness and the efficiency of the generated sets of rules for relationship discovery.