  The cross-site linking function is widely adopted by online social networks
(OSNs). This function allows a user to link her account on one OSN to her
accounts on other OSNs. Thus, users are able to sign in with the linked
accounts, share contents among these accounts and import friends from them. It
leads to the service integration of different OSNs. This integration not only
provides convenience for users to manage accounts of different OSNs, but also
introduces usefulness to OSNs that adopt the cross-site linking function. In
this paper, we investigate this usefulness based on users' data collected from
a popular OSN called Medium. We conduct a thorough analysis on its social
graph, and find that the service integration brought by the cross-site linking
function is able to change Medium's social graph structure and attract a large
number of new users. However, almost none of the new users would become high
PageRank users (PageRank is used to measure a user's influence in an OSN). To
solve this problem, we build a machine-learning-based model to predict high
PageRank users in Medium based on their Twitter data only. This model achieves
a high F1-score of 0.942 and a high area under the curve (AUC) of 0.986. Based
on it, we design a system to assist new OSNs to identify and attract high
PageRank users from other well-established OSNs through the cross-site linking
function.
