Ontology-Aware Classification and Association Rule Mining for Interest and Link Prediction in Social Networks
Previous work on analysis of friendship networks has identified ways in which graph features can be used for prediction of link existence and persistence, and shown that features of user pairs such as shared interests can marginally improve the precision and recall of link prediction. This marginal improvement has, to date, been severely limited by the flat representation used for interest taxonomies. We present an approach towards integration of such graph features with ontology-enriched numerical and nominal features (based on interest hierarchies) and on itemset size-sensitive associations found using interest data. A test bed previously developed using the social network and weblogging service LiveJournal is extended using this integrative approach. Our results show how this semantically integrative approach to link mining yields a boost in precision and recall of known friendships when applied to this test bed. We conclude with a discussion of link-dependent features and how an integrative constructive induction framework can be extended to incorporate temporal fluents for link prediction, interest prediction, and annotation in social networks.