Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15325 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914271910166528 |
|---|---|
| author | Li, Chaojun Fang, Hao |
| author_facet | Li, Chaojun Fang, Hao |
| contents | Dynamic community detection plays a crucial role in understanding the temporal evolution of community structures in complex networks. Existing methods based on nonnegative tensor RESCAL decomposition typically require the decomposition rank to equal the number of communities, which limits model flexibility. This paper proposes an improved MLP-enhanced nonnegative tensor decomposition model (MLP-NTD) that incorporates a multilayer perceptron (MLP) after RESCAL decomposition for community mapping, thereby decoupling the decomposition rank from the number of communities. The framework optimizes model parameters through a reconstruction loss function, which preserves the ability to capture dynamic community evolution while significantly improving the accuracy and robustness of community partitioning. Experimental results on multiple real-world dynamic network datasets demonstrate that MLP-NTD outperforms state-of-the-art methods in terms of modularity, validating the effectiveness of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15325 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MLP-Enhanced Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection Li, Chaojun Fang, Hao Social and Information Networks Dynamic community detection plays a crucial role in understanding the temporal evolution of community structures in complex networks. Existing methods based on nonnegative tensor RESCAL decomposition typically require the decomposition rank to equal the number of communities, which limits model flexibility. This paper proposes an improved MLP-enhanced nonnegative tensor decomposition model (MLP-NTD) that incorporates a multilayer perceptron (MLP) after RESCAL decomposition for community mapping, thereby decoupling the decomposition rank from the number of communities. The framework optimizes model parameters through a reconstruction loss function, which preserves the ability to capture dynamic community evolution while significantly improving the accuracy and robustness of community partitioning. Experimental results on multiple real-world dynamic network datasets demonstrate that MLP-NTD outperforms state-of-the-art methods in terms of modularity, validating the effectiveness of the proposed approach. |
| title | MLP-Enhanced Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2601.15325 |