MLP-Enhanced Nonnegative Tensor RESCAL Decomposition for Dynamic Community Detection

TL;DR

This paper introduces MLP-NTD, an improved nonnegative tensor RESCAL decomposition method with an MLP component, enabling more flexible and accurate dynamic community detection in complex networks.

Contribution

It proposes a novel MLP-enhanced tensor decomposition model that decouples the rank from the number of communities, improving dynamic community detection accuracy.

Findings

Outperforms state-of-the-art methods in modularity on real datasets

Enhances robustness and flexibility of community detection

Maintains dynamic community evolution capture

Abstract

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.