MNTD: An Efficient Dynamic Community Detector Based on Nonnegative Tensor Decomposition

TL;DR

This paper introduces MNTD, a novel method combining nonnegative tensor RESCAL decomposition with modularity maximization to improve dynamic community detection accuracy in complex networks.

Contribution

The paper presents a new dynamic community detection model that effectively captures temporal community evolution and enhances segmentation precision over existing methods.

Findings

MNTD outperforms state-of-the-art methods in accuracy on real-world datasets.

The model effectively captures community persistence and transformation over time.

Incorporating initial community structures improves segmentation accuracy.

Abstract

Dynamic community detection is crucial for elucidating the temporal evolution of social structures, information dissemination, and interactive behaviors within complex networks. Nonnegative matrix factorization provides an efficient framework for identifying communities in static networks but fall short in depicting temporal variations in community affiliations. To solve this problem, this paper proposes a Modularity maximization-incorporated Nonnegative Tensor RESCAL Decomposition (MNTD) model for dynamic community detection. This method serves two primary functions: a) Nonnegative tensor RESCAL decomposition extracts latent community structures in different time slots, highlighting the persistence and transformation of communities; and b) Incorporating an initial community structure into the modularity maximization algorithm, facilitating more precise community segmentations. Comparative analysis of real-world datasets shows that the MNTD is superior to state-of-the-art dynamic community detection methods in the accuracy of community detection.