I. Introduction
ONLINE Social Networks (OSNs) have become an essential part of modern life. Billions of users connect and share information using OSNs such as Facebook and Twitter. Graphs obtained from these OSNs can provide useful insights on various fundamental societal phenomena such as epidemiology, information dissemination, marketing, and sentiment flow [2], [12], [23], [55], [56]. Various analysis methods [8], [13], [22], [37], [40] have been applied to OSNs by explicitly exploring its graph structure, such as clustering analysis for automatically identifying online communities and node influence analysis for recognizing the influential nodes in social networks. The basis of all these analysis is to represent a social network graph by an adjacency matrix and then represent individual nodes by vectors derived from the top eigenvectors of the adjacency matrix. Thus, all these analysis methods require real social network graphs.