Deep Autoencoder-like non-negative matrix factorization with graph regularized for link prediction in dynamic networks

Lv, Laishui; Bardou, Dalal; Liu, Yanqiu; Hu, Peng

doi:10.1016/j.asoc.2023.110832

Cited by 3 publications

(2 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method effectively combines the encoder and decoder components under a single loss function framework. Ye and his colleagues [25] took an additional step and innovatively developed a new category of deep autoencoder NMF (DANMF) models for community detection tasks. This model extends Sun's NNSED approach by constructing a framework like that of a deep autoencoder.…”

Section: Learning-model-based Community Detectionmentioning

confidence: 99%

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

Huang,

Zhong

2024

Mathematics

View full text Add to dashboard Cite

Community structure is a significant characteristic of complex networks, and community detection has valuable applications in network structure analysis. Non-negative matrix factorization (NMF) is a key set of algorithms used to solve the community detection issue. Nevertheless, the localization of feature vectors in the adjacency matrix, which represents the characteristics of complex network structures, frequently leads to the failure of NMF-based approaches when the data matrix has a low density. This paper presents a novel algorithm for detecting sparse network communities using non-negative matrix factorization (NMF). The algorithm utilizes local feature vectors to represent the original network topological features and learns regularization matrices. The resulting feature matrices effectively reveal the global structure of the data matrix, demonstrating enhanced feature expression capabilities. The regularized data matrix resolves the issue of localized feature vectors caused by sparsity or noise, in contrast to the adjacency matrix. The approach has superior accuracy in detecting community structures compared to standard NMF-based community detection algorithms, as evidenced by experimental findings on both simulated and real-world networks.

show abstract

Section: Learning-model-based Community Detectionmentioning

confidence: 99%

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

Huang,

Zhong

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…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]. By employing layer-by-layer encoding, a high-dimensional dataset is transformed into a low-dimensional encoding, which can then be reconstructed back to its original high-dimensional form using layer-by-layer decoding.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Yu,

Wang

et al. 2024

Mathematics

View full text Add to dashboard Cite

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.

show abstract

Diverse joint nonnegative matrix tri-factorization for attributed graph clustering

Mohammadi,

Seyedi,

Akhlaghian Tab

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

Deep Autoencoder-like non-negative matrix factorization with graph regularized for link prediction in dynamic networks

Cited by 3 publications

References 59 publications

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

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

Diverse joint nonnegative matrix tri-factorization for attributed graph clustering

Contact Info

Product

Resources

About