Abstract: 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.

Key words: Interactive similarity,Fine grain,Online social network,Community detection