Multidimensional community discovering in heterogeneous social networks is an important issue. Many approaches have been proposed for community discovering in heterogeneous networks. However, they have focused only on topological properties of these networks, ignoring the embedded semantic information. As the solution to this information glut limit, we propose, in this article, a new multidimensional community discovering approach, which incorporates the multiple types of objects and relationships, derived from a heterogeneous networks. First, we propose to construct the concept lattice family CLF to represent the different objects and relations of the heterogeneous social networks based on the relational concept analysis techniques. Then after we introduce a new algorithm that explores such CLF and extract the multidimensional communities. Carried out experiments on real datasets enhance the effectiveness of our proposal and open promising issues.

中文翻译：

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异构社交网络中的多维社区发现

异构社交网络中的多维社区发现是一个重要问题。已经提出了许多方法用于异构网络中的社区发现。然而，他们只关注这些网络的拓扑特性，而忽略了嵌入的语义信息。作为这种信息过剩限制的解决方案，我们在本文中提出了一种新的多维社区发现方法，该方法结合了来自异构网络的多种类型的对象和关系。首先，我们提出基于关系概念分析技术构建概念格族CLF来表示异构社交网络的不同对象和关系。然后我们引入了一种新的算法来探索这种 CLF 并提取多维社区。