In this paper, we try to systematically study how to perform doctor recommendation in mobile Medical Social Networks (
-MSNs). Specifically, employing a real-world medical dataset as the source in our study, we first mine doctor-patient ties/relationships via Time-constraint Probability Factor Graph model (TPFG), and then define the transition probability matrix between neighbor nodes. Finally, we propose a doctor recommendation model via Random Walk with Restart (
-Model. Our real experiments validate the effectiveness of the proposed method. Experimental results show that we obtain the good accuracies of mining doctor-patient relationships from the network, the performance of doctor recommendation is also better than the baseline algorithms: traditional Reduced SVM (RSVM) method and IDRModel.