基于张量分析的链接聚类算法的研究*

针对复杂信息网络中多链接高维数据聚类难以处理且效率较低问题，提出了一种新颖的基于高阶张量分析方法和模块化网络分析方法相结合的链接聚类算法。利用模块化方法分析网络，利用张量的形式表示多维的复杂的多链接数据，利用Tucker张量分解的方法对数据降维处理，降低了算法的时间和空间复杂度。并在复杂网络环境下，通过实验验证了算法的有效性和健壮性。

Research about link clustering algorithmic based on tensor analysis

Because the multi-link high-dimensional data clustering problem of complex information networks is difficult to handle and less efficient, so we propose a novel link clustering algorithm based on higher order tensor analysis methods and modularity network analysis. Using modularity approach to analysis networks, using multi-dimensional tensor expressed in the form of complex multi-link data, using Tucker tensor decomposition method to reduce the dimensions of the data and the time and space complexity of the algorithm. The effectiveness and robustness of the algorithmic is tested in complex network environment.