中文摘要：

以经典的CommonNeighbor算法为例，提出了一种基于社区划分的差分化节点角色的链路预测模型，该模型首先采用Clauset-Newman-Moore算法挖掘社会网络结构属性，同时引入节点连接度和社区整体参与度的定义，差分处理社区内外邻接节点和不同社区的贡献，采用有监督的学习训练方法分别对社区内节点对和社区间节点对进行链路预测．人工网络和真实网络中的实验证明，该模型能够提高基于相似度算法对节点对链路预测的准确率，并为该类模型的研究提供一种新的方案．

英文摘要：

This paper proposes a new link prediction model basing on community partition to differentiate diverse contribution of different vertexes which taking Common Neighbor algorithms as examples. Firstly, the model uses Clauset-Newman-Moore algorithm to partition community and generates pairs via a different aspect, then applies different weight to inner-community and outer-community neighbor vertexes, finally provides a supervised training method to infer the missing links. The experimental results demonstrate that the algorithm can not only raises prediction precision but also provides a kind of effect method for research of similarity link predication.