动态多模网络中演化社区发现算法改进

Identifying evolving groups in dynamic multi-mode networks

在动态多模式网络中发现社区可以帮助人们了解网络的结构属性,解决数据不足和不平衡问题,并且可以协助解决市场营销和发现重要参与者的问题。一般来说,网络和它的社区结构是不均匀进化的。通过使用时态信息来分析多模网络,分析时态正则化架构和它的收敛属性。提出的算法可以解释为一个迭代的潜在语义分析过程,允许扩展到处理带有参与者属性和模内联系的网络。

Identifying communities in a multi-mode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and its group structure often evolve unevenly. The paper tried to address this problem by employing the temporal information to analyze a multi-mode network. A temporally-regularized framework and its convergence property were carefully studied. It showed that the algorithm can be interpreted as an iterative latent semantic analysis process, which allows for extensions to handle networks with actor attributes arid within-mode interactions.