Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5103387 | Physica A: Statistical Mechanics and its Applications | 2017 | 25 Pages |
Abstract
At first, we bring forward a simple but useful method to address the problem. Then, we design a structured deep convolutional neural network (CNN) model to better detect communities in TIN. By gradually removing edges of the real-world networks, we show the effectiveness and robustness of our structured deep model on a variety of real-world networks. Moreover, we find that the appropriate choice of hop counts can improve the performance of our deep model in some degree. Finally, experimental results conducted on synthetic data sets also show the good performance of our proposed deep CNN model.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Xin Xin, Chaokun Wang, Xiang Ying, Boyang Wang,