Abstract: Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. However, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored until recently. The semi-supervised classification problem on query-document heterogeneous information network which incorporate the bipartite graph with the content information from both sides is considered. In order to strengthen the network structure, class information of sample nodes is introduced. A semi-supervised learning algorithm based on two frameworks including the novel graph-based regularization framework and the iterative framework is investigated. In the regularization framework, a new cost function to consider the direct relationship between two entity sets and the content information from both sides which leads to a significant improvement over the baseline methods is developed. Experimental results demonstrate that proposed method achieves the best performance with consistent and promising improvements.