中文摘要：

现有的微博社交网络社区挖掘方法多是基于网络结构进行，忽略了节点本身行为的重要性，并且不能同时实现对大规模复杂网络结构适应性和社区挖掘的高效性。为缓解上述问题，提出了一种基于网络距离和内容相似度的微博社交网络社区划分方法，该方法在考虑微博社交网络结构的同时兼顾了网络中节点的历史微博内容，通过对历史微博数据的分析提高社区划分的精确度。文中对Louvain算法和其模块性的修改使用，保证了该方法能够处理大规模网络数据，同时又能保证社区挖掘的效率。实验证明，该方法能够高效地挖掘微博网络社区结构，对学术研究和商业应用都有十分重要的意义。

英文摘要：

Existing micro-blog social network community mining methods are based on the network structure, ignoring the importance of node＇s behavior, and can not guarantee the adaptability on large-scale complex network structure and the efficiency of community mining. To alleviate these problems, a new method ABDC is proposed for the community network of micro-blog based on the network distance and content similarity, the method considers the structure of the social network of micro-blog at the same time taking into account the historical blog content of the node in the network, improved the accuracy of community division through analysis the historical micro-blog data, In this paper, the Louvain algorithm and its modularity are modified and used to ensure that the method can deal with large scale network data, and get high efficiency of community mining. Experiments show that the method can efficiently mine the community structure of micro-blog network, which has great significance for academic research and business applications.