Identifying user clicks from a large number of measured HTTP requests is the fundamental task for web usage mining, which is important for web administrators and developers. Nowadays, the prevalent parallel web browsing behavior caused by multi-tab web browsers renders accurate user click identification from massive requests a great challenge. In this paper, we propose a dependency graph model to describe the complicated web browsing behavior. Based on this model, we develop two algorithms to establish the dependency graph for measured requests, and identify user clicks by comparing their probabilities of being primary requests with a self-learned threshold. We evaluate our method with a large dataset collected from a real world mobile core network. The experimental results show that our method can achieve high accurate user clicks identification.