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In this paper, we investigate how user's online behavior (e.g., making friendships) and their offline activity (e.g., check-ins) affected each other by leveraging the data collected from LBSN. First, we use vectors to represent nodes and define popularity entropy for each node to weigh their popularities and the impact on forming a new edge. Then, we propose an algorithm to calculate the weight of each edge based on our findings that the more overlap of linked nodes they have, the heavier weight the edge has and the more popular the nodes in their overlap are, the lighter the weight of the edge is. Finally, we conduct link prediction by using the random walk with restart method considering the effect of every node and every edge. Experimental results show that user's activity in virtual world and physical world do really have great impact on each other.

Investigating How User's Activities in Both Virtual and Physical World Impact Each Other Leveraging LBSN Data