基于交互相似度的细粒度社群发掘方法  

Community Development Method Based on Interactive Similarity

作　　者：张星[1] 於志文[1] 梁韵基[1] 郭斌[1] 

机构地区：[1]西北工业大学计算机学院,西安710129

出　　处：《计算机科学》2014年第4期215-218,229,共5页Computer Science

基　　金：国家重点基础研究发展计划(2012CB316400);国家自然科学基金(61222209;61103063);教育部高等学校博士学科点专项科研基金(博导类)(20126102110043);陕西省自然科学基础研究计划项目(2012JQ8028)资助

摘　　要：发现在线社交网络中的社群结构有助于深入研究和分析信息传播规律,同时在社会推荐、群体特征发现等应用领域具有重要的意义。但是现有的社群结构发掘方法多忽略了用户之间的社会属性,导致获取的社群结构难以反映细粒度的结构特征。文中将用户的社会属性引入到社群结构发掘算法中。为了衡量用户的社会交互属性,提出了用户交互相似度模型。基于用户交互相似度模型,提出了一种面向在线社交网络的细粒度社群发掘方法。该算法可以有效衡量用户之间的社会属性,通过层次聚类的手段获得不同粒度的社群,并过滤无关数据。为了验证算法的有效性,以社交网站人人网的用户交互记录为数据集,比较了与其他社区挖掘算法的性能差异。实验结果表明,该方法发掘出的细粒度社群具有较高的准确性,在发现社群之间的不同话题上有着较好的应用。It was found that online social network community structure contributes to in-depth research of information propagation,social recommendation and the application of group identity discovery.Existing community structure excavation method ignores the many social attributes among users,which makes it difficult that the obtained community structure reflects the fine-grained structure.We combined user＇s social attributes into community structures excavated algorithm,then proposed the user interaction model.in order to measure the user＇s social interaction properties.We proposed a community development method based on interactive similarity.The algorithm can effectively measure social attributes between users,get different size groups through hierarchical clustering,and filter noise data.In order to verify the effectiveness of the algorithm,we collected user interaction recorded from social networking site as data sets,compared the performance differences with other community mining algorithm.The experimental results show that this method discovers fine-grained communities with high accuracy,and may be used to discover different topic between communities.

关 键 词：交互相似度 细粒度 在线社交网络 社群发掘 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]