With the high dimensionality of data, the method of tensor decomposition has attracted much attention in the field of data research and analysis. The tensor decomposition is well reflected in the study of high-dimensi...With the high dimensionality of data, the method of tensor decomposition has attracted much attention in the field of data research and analysis. The tensor decomposition is well reflected in the study of high-dimensional data. The existing research uses the results of tensor decomposition to conduct community discovery. Based on the existing research, this paper presents a method to study community evolution using the results of tensor decomposition. The feature matrix obtained by the tensor decomposition algorithm was analyzed, and the real-time activity of the community with the feature matrix with time slice direction was studied to obtain the event process of community evolution. Experimental results in real data sets show that this method can well analyze dynamic events in the dataset and community evolution events.展开更多
文摘With the high dimensionality of data, the method of tensor decomposition has attracted much attention in the field of data research and analysis. The tensor decomposition is well reflected in the study of high-dimensional data. The existing research uses the results of tensor decomposition to conduct community discovery. Based on the existing research, this paper presents a method to study community evolution using the results of tensor decomposition. The feature matrix obtained by the tensor decomposition algorithm was analyzed, and the real-time activity of the community with the feature matrix with time slice direction was studied to obtain the event process of community evolution. Experimental results in real data sets show that this method can well analyze dynamic events in the dataset and community evolution events.
