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.
Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem largely studied so far is Link Prediction, i.e. the problem of predicting new upcoming connections in the network. However, one aspect of complex networks has been disregarded so far: real networks are often multidimensional, i.e. multiple connections may reside between any two nodes. In this context, we define the problem of Multidimensional Link Prediction, and we introduce several predictors based on structural analysis of the networks. We present the results obtained on real networks, showing the performances of both the introduced multidimensional versions of the Common Neighbors and Adamic-Adar, and the derived predictors aimed at capturing the multidimensional and temporal information extracted from the data. Our findings show that the evolution of multidimensional networks can be predicted, and that supervised models may improve the accuracy of underlying unsupervised predictors, if used in conjunction with them.