  Due to the proliferation of online social networks (OSNs), users find
themselves participating in multiple OSNs. These users leave their activity
traces as they maintain friendships and interact with other users in these
OSNs. In this work, we analyze how users maintain friendship in multiple OSNs
by studying users who have accounts in both Twitter and Instagram.
Specifically, we study the similarity of a user's friendship and the evenness
of friendship distribution in multiple OSNs. Our study shows that most users in
Twitter and Instagram prefer to maintain different friendships in the two OSNs,
keeping only a small clique of common friends in across the OSNs. Based upon
our empirical study, we conduct link prediction experiments to predict missing
friendship links in multiple OSNs using the neighborhood features, neighborhood
friendship maintenance features and cross-link features. Our link prediction
experiments shows that un- supervised methods can yield good accuracy in
predicting links in one OSN using another OSN data and the link prediction
accuracy can be further improved using supervised method with friendship
maintenance and others measures as features.
