微博网络是新兴的覆盖海量用户、涉及广泛话题并具有复杂重叠社群结构的多模网络。在深入研究微博网络各类实体和属性内在联系的基础上，提出了以用户-话题关系为主要划分原则的重叠社群表达模型及相应的社群结构发现算法。该方法不仅考虑网络中的用户-话题关系，还融合了这一网络特有的用户关注关系、博文评论与转发关系等所形成的复合网络关系。同时，改进了传统的社群隶属矩阵表述模型，通过引入虚拟社群，使隶属矩阵不仅合理反映个体对社群的隶属度，同时标识了个体在社群中的核心度。通过基于新浪微博数据集的实验验证，结果表明：该模型与方法能够高效合理地刻画该数据集包含的重叠社群结构，实验结果具有良好的可解释性，所提出的模型和算法可以有效地应用于类似多模网络社群划分和演化分析研究中。%Micro-Blog cyberspace is a booming multiple mode network of numerous overlapping communities covering huge amount of users and topics relating to the nature, the society and the everyday life. Based on in depth analysis on the entities and inherent relationships among the network, this paper purposes a user-topic relation dominated structural module for overlapping community representation and detection, and also infuses the follow relationship along with the blog-forward and blog-comment relationship into the module. By introducing a virtual community into the actual communities of the network, the paper also puts forward an improved global belongingness matrix as user’s role representation which has the ability to properly describe a user’s degree of participation and importance in the network. Experimental results on Sina’s micro-blog dataset show that the new method is favorable and efficient for finding meaningful communities from the micro-blog. Furthermore, the proposed module and algorithms can be adapted in various ways for similar social network analysis and helpful for community evolution research.
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