Abstract: An information transfer classification algorithm based on semi-supervised learning is proposed in this paper to partition the network with labeled nodes and unlabeled nodes into different clusters.The classification parameters of all unlabeled nodes in network are firstly determined in terms of the suggested approach，and the clustering results can be obtained by iteratively computing these parameters.The analysis of experimental data has proven that this algorithm has better effect on semi-supervised classification.

摘要： 为了对有标签和无标签节点混合的网络进行分类，给出了一种基于半监督学习的信息传递分类算法，算法首先确定网络中无标签节点的分类参数，然后通过对网络中所有无标签节点进行有限次的迭代计算，可以对所有节点进行分类。实验数据分析证明了该算法在进行半监督分类时具有比较好的效果。