Community detection in networks through a deep robust auto-encoder nonnegative matrix factorization

Al-sharoa, Esraa; Rahahleh, Baraa

doi:10.1016/j.engappai.2022.105657

Cited by 12 publications

(2 citation statements)

References 51 publications

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“…Bio-inspired optimization has successfully solved the problem of community detection in network that we observe from the work Osaba et al ( 2020 ) where the authors present in detail the problem of community detection from view of bio-inspired computation. Works like Al-sharoa and Rahahleh ( 2023 ) show a deep robust auto-encoder nonnegative matrix factorization (DRANMF) approach consisting of a deep structured decoder and encoder components to detect the community structure in networks.…”

Section: Related Workmentioning

confidence: 99%

Efficient community detection in multilayer networks using boolean compositions

Santra,

Irany,

Madduri

et al. 2023

Front. Big Data

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Networks (or graphs) are used to model the dyadic relations between entities in complex systems. Analyzing the properties of the networks reveal important characteristics of the underlying system. However, in many disciplines, including social sciences, bioinformatics, and technological systems, multiple relations exist between entities. In such cases, a simple graph is not sufficient to model these multiple relations, and a multilayer network is a more appropriate model. In this paper, we explore community detection in multilayer networks. Specifically, we propose a novel network decoupling strategy for efficiently combining the communities in the different layers using the Boolean primitives AND, OR, and NOT. Our proposed method, network decoupling, is based on analyzing the communities in each network layer individually and then aggregating the analysis results. We (i) describe our network decoupling algorithms for finding communities, (ii) present how network decoupling can be used to express different types of communities in multilayer networks, and (iii) demonstrate the effectiveness of using network decoupling for detecting communities in real-world and synthetic data sets. Compared to other algorithms for detecting communities in multilayer networks, our proposed network decoupling method requires significantly lower computation time while producing results of high accuracy. Based on these results, we anticipate that our proposed network decoupling technique will enable a more detailed analysis of multilayer networks in an efficient manner.

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Section: Related Workmentioning

confidence: 99%

Efficient community detection in multilayer networks using boolean compositions

Santra,

Irany,

Madduri

et al. 2023

Front. Big Data

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“…The deep autoencoder, an extensively employed unsupervised learning algorithm in the field of deep learning [19], is capable of performing various tasks such as feature extraction, dimensionality reduction, and data reconstruction. In addition, it can also bridge the gap between low-level to highlevel networks for optimal community detection [20]. The deep autoencoder consists of two components: an encoder component and a decoder component [21].…”

Section: Introductionmentioning

confidence: 99%

Constrained Symmetric Non-Negative Matrix Factorization with Deep Autoencoders for Community Detection

Zhang,

Yu,

Wang

et al. 2024

Mathematics

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Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces a novel constrained symmetric non-negative matrix factorization with deep autoencoders (CSDNMF) as a solution to this issue. The model possesses the following advantages: (1) By integrating a deep autoencoder to discern the latent attributes bridging the original network and community assignments, it adeptly captures hierarchical information. (2) Introducing a graph regularizer facilitates a thorough comprehension of the community structure inherent within the target network. (3) By integrating a symmetry regularizer, the model’s capacity to learn undirected networks is augmented, thereby facilitating the precise detection of symmetry within the target network. The proposed CSDNMF model exhibits superior performance in community detection when compared to state-of-the-art models, as demonstrated by eight experimental results conducted on real-world networks.

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